AMD Core Math Library (ACML) Version 4.2.0 c 2003-2008 Advanced Micro Devices, Inc., Numerical Algorithms Group Ltd. Copyright AMD, the AMD Arrow logo, AMD Opteron, AMD Athlon and combinations thereof are trademarks of Advanced Micro Devices, Inc. i Short Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 General Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 BLAS: Basic Linear Algebra Subprograms . . . . . . . . . . . . . 19 4 LAPACK: Package of Linear Algebra Subroutines . . . . . . . . 20 5 Fast Fourier Transforms (FFTs) . . . . . . . . . . . . . . . . . . . . 24 6 Random Number Generators . . . . . . . . . . . . . . . . . . . . . . . 75 7 ACML MV: Fast Math and Fast Vector Math Library . . . . 163 8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Routine Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 ii Table of Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 General Information . . . . . . . . . . . . . . . . . . . . . . . 2 2.1 2.2 Determining the best ACML version for your system . . . . . . . . . . . 2 Accessing the Library (Linux) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.1 Accessing the Library under Linux using GNU gfortran/gcc ......................................................... 4 2.2.2 Accessing the Library under Linux using PGI compilers pgf77/pgf90/pgcc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.3 Accessing the Library under Linux using PathScale compilers pathf90/pathcc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.4 Accessing the Library under Linux using the NAGWare f95 compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.5 Accessing the Library under Linux using the Intel ifort compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.6 Accessing the Library under Linux using Absoft af90 . . . . . . 7 2.2.7 Accessing the Library under Linux using compilers other than GNU, PGI, PathScale, NAGWare, Intel or Absoft . . . . . . . . . . . 8 2.3 Accessing the Library (Microsoft Windows) . . . . . . . . . . . . . . . . . . . 8 2.3.1 Accessing the Library under 32-bit Windows using PGI compilers pgf77/pgf90/Microsoft C . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3.2 Accessing the Library under 32-bit Windows using Microsoft C or Intel Fortran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.3 Accessing the Library under 32-bit Windows using the Compaq Visual Fortran compiler . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.4 Accessing the Library under 32-bit Windows using the Salford FTN95 compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.5 Accessing the Library under 64-bit Windows using PGI compilers pgf77/pgf90/pgcc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.6 Accessing the Library under 64-bit Windows using Microsoft C or Intel Fortran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Accessing the Library (Solaris) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 Accessing the Library under Solaris . . . . . . . . . . . . . . . . . . . . . 12 2.5 ACML FORTRAN and C interfaces . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.6 ACML variants using 64-bit integer (INTEGER*8) arguments . . 15 2.7 Library Version and Build Information . . . . . . . . . . . . . . . . . . . . . . . 16 2.8 Library Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.9 Example programs calling ACML . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.10 Example ACML programs demonstrating performance . . . . . . . 17 3 BLAS: Basic Linear Algebra Subprograms . . 19 iii 4 LAPACK: Package of Linear Algebra Subroutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1 4.2 4.3 4.4 5 Introduction to LAPACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference sources for LAPACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LAPACK block sizes, ILAENV and ILAENVSET . . . . . . . . . . . . . IEEE exceptions and LAPACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 20 21 23 Fast Fourier Transforms (FFTs) . . . . . . . . . . . 24 5.1 Introduction to FFTs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Transform definitions and Storage for Complex Data . . . . . 5.1.2 Transform definitions and Storage for Real Data . . . . . . . . . 5.1.3 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Default and Generated Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 FFTs on Complex Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 FFT of a single sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 FFT of multiple complex sequences . . . . . . . . . . . . . . . . . . . . . ZFFT1M Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1M Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1MX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1MX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 2D FFT of two-dimensional arrays of data . . . . . . . . . . . . . . . ZFFT2D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT2D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT2DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT2DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 3D FFT of three-dimensional arrays of data . . . . . . . . . . . . . ZFFT3D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3D Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT3DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3DX Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT3DY Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3DY Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 FFTs on real and Hermitian data sequences . . . . . . . . . . . . . . . . . . 5.3.1 FFT of single sequences of real data . . . . . . . . . . . . . . . . . . . . . DZFFT Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SCFFT Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 FFT of multiple sequences of real data . . . . . . . . . . . . . . . . . . DZFFTM Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . SCFFTM Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 FFT of single Hermitian sequences . . . . . . . . . . . . . . . . . . . . . . ZDFFT Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSFFT Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 FFT of multiple Hermitian sequences. . . . . . . . . . . . . . . . . . . . ZDFFTM Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 24 25 25 26 27 27 28 29 30 32 34 35 37 39 41 43 44 45 46 49 52 53 54 56 58 60 63 66 67 67 68 69 69 70 71 71 72 73 73 iv CSFFTM Routine Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6 Random Number Generators . . . . . . . . . . . . . . 75 6.1 Base Generators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.1.1 Initialization of the Base Generators . . . . . . . . . . . . . . . . . . . . 76 DRANDINITIALIZE / SRANDINITIALIZE . . . . . . . . . . . . . . . . . . . . . . 78 DRANDINITIALIZEBBS / SRANDINITIALIZEBBS . . . . . . . . . . . . . . . 81 6.1.2 Calling the Base Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 DRANDBLUMBLUMSHUB / SRANDBLUMBLUMSHUB . . . . . . . . . . . . . . . . . 83 6.1.3 Basic NAG Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.1.4 Wichmann-Hill Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.1.5 Mersenne Twister . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.1.6 L’Ecuyer’s Combined Recursive Generator . . . . . . . . . . . . . . . 85 6.1.7 Blum-Blum-Shub Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.1.8 User Supplied Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 DRANDINITIALIZEUSER / SRANDINITIALIZEUSER . . . . . . . . . . . . . 87 UINI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 UGEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.2 Multiple Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.2.1 Using Different Seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.2.2 Using Different Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.2.3 Skip Ahead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 DRANDSKIPAHEAD / SRANDSKIPAHEAD . . . . . . . . . . . . . . . . . . . . . . . . 92 6.2.4 Leap Frogging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 DRANDLEAPFROG / SRANDLEAPFROG . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6.3 Distribution Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.3.1 Continuous Univariate Distributions . . . . . . . . . . . . . . . . . . . . . 97 DRANDBETA / SRANDBETA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 DRANDCAUCHY / SRANDCAUCHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 DRANDCHISQUARED / SRANDCHISQUARED . . . . . . . . . . . . . . . . . . . . . 101 DRANDEXPONENTIAL / SRANDEXPONENTIAL . . . . . . . . . . . . . . . . . . . 103 DRANDF / SRANDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 DRANDGAMMA / SRANDGAMMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 DRANDGAUSSIAN / DRANDGAUSSIAN . . . . . . . . . . . . . . . . . . . . . . . . . 109 DRANDLOGISTIC / SRANDLOGISTIC . . . . . . . . . . . . . . . . . . . . . . . . . 111 DRANDLOGNORMAL / SRANDLOGNORMAL . . . . . . . . . . . . . . . . . . . . . . . 113 DRANDSTUDENTST / SRANDSTUDENTST . . . . . . . . . . . . . . . . . . . . . . . 115 DRANDTRIANGULAR / SRANDTRIANGULAR . . . . . . . . . . . . . . . . . . . . . 117 DRANDUNIFORM / SRANDUNIFORM. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 DRANDVONMISES / SRANDVONMISES . . . . . . . . . . . . . . . . . . . . . . . . . 121 DRANDWEIBULL / SRANDWEIBULL. . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.3.2 Discrete Univariate Distributions . . . . . . . . . . . . . . . . . . . . . . 125 DRANDBINOMIAL / SRANDBINOMIAL . . . . . . . . . . . . . . . . . . . . . . . . . 125 DRANDGEOMETRIC / SRANDGEOMETRIC . . . . . . . . . . . . . . . . . . . . . . . 127 DRANDHYPERGEOMETRIC / SRANDHYPERGEOMETRIC . . . . . . . . . . . . 129 DRANDNEGATIVEBINOMIAL / SRANDNEGATIVEBINOMIAL. . . . . . . . 131 DRANDPOISSON / SRANDPOISSON. . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 DRANDDISCRETEUNIFORM / SRANDDISCRETEUNIFORM . . . . . . . . . . 135 v DRANDGENERALDISCRETE / SRANDGENERALDISCRETE . . . . . . . . . . 137 DRANDBINOMIALREFERENCE / SRANDBINOMIALREFERENCE . . . . . 139 DRANDGEOMETRICREFERENCE / SRANDGEOMETRICREFERENCE . . . 141 DRANDHYPERGEOMETRICREFERENCE / SRANDHYPERGEOMETRICREFERENCE . . . . . . . . . . . . . . . . . . . . . 143 DRANDNEGATIVEBINOMIALREFERENCE / SRANDNEGATIVEBINOMIALREFERENCE . . . . . . . . . . . . . . . . . . . 145 DRANDPOISSONREFERENCE / SRANDPOISSONREFERENCE. . . . . . . . 147 6.3.3 Continuous Multivariate Distributions . . . . . . . . . . . . . . . . . . 149 DRANDMULTINORMAL / SRANDMULTINORMAL . . . . . . . . . . . . . . . . . . . 149 DRANDMULTISTUDENTST / SRANDMULTISTUDENTST . . . . . . . . . . . . 151 DRANDMULTINORMALR / SRANDMULTINORMALR . . . . . . . . . . . . . . . . 153 DRANDMULTISTUDENTSTR / SRANDMULTISTUDENTSTR . . . . . . . . . . 155 DRANDMULTINORMALREFERENCE / SRANDMULTINORMALREFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 DRANDMULTISTUDENTSTREFERENCE / SRANDMULTISTUDENTSTREFERENCE . . . . . . . . . . . . . . . . . . . . . 159 6.3.4 Discrete Multivariate Distributions . . . . . . . . . . . . . . . . . . . . . 161 DRANDMULTINOMIAL / SRANDMULTINOMIAL . . . . . . . . . . . . . . . . . . . 161 7 ACML MV: Fast Math and Fast Vector Math Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 7.1 Introduction to ACML MV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Weak Aliases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Defined Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Fast Basic Math Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastcos: fast double precision Cosine . . . . . . . . . . . . . . . . . . . . . . . . . . fastcosf: fast single precision Cosine . . . . . . . . . . . . . . . . . . . . . . . . . . fastexp: fast double precision exponential function . . . . . . . . . . . . . fastexpf: fast single precision exponential function . . . . . . . . . . . . . fastlog: fast double precision natural logarithm function . . . . . . . . fastlogf: fast single precision natural logarithm function . . . . . . . . fastlog10: fast double precision base-10 logarithm function . . . . . fastlog10f: fast single precision base-10 logarithm function . . . . . . fastlog2: fast double precision base-2 logarithm function . . . . . . . . fastlog2f: fast single precision base-2 logarithm function . . . . . . . . fastpow: fast double precision power function. . . . . . . . . . . . . . . . . . fastpowf: fast single precision power function . . . . . . . . . . . . . . . . . . fastsin: fast double precision Sine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsinf: fast single precision Sine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsincos: fast double precision Sine and Cosine . . . . . . . . . . . . . . . fastsincosf: fast single precision Sine and Cosine . . . . . . . . . . . . . . . 7.3 Fast Vector Math Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrd2 cos: Two-valued double precision Cosine . . . . . . . . . . . . . . . . . vrd4 cos: Four-valued double precision Cosine . . . . . . . . . . . . . . . . . vrda cos: Array double precision Cosine . . . . . . . . . . . . . . . . . . . . . . vrs4 cosf: Four-valued single precision Cosine . . . . . . . . . . . . . . . . . 163 163 164 164 165 165 166 167 168 169 170 171 172 173 174 175 177 179 180 181 182 183 183 184 185 186 vi vrsa cosf: Array single precision Cosine . . . . . . . . . . . . . . . . . . . . . . . vrd2 exp: Two-valued double precision exponential function . . . . vrd4 exp: Four-valued double precision exponential function . . . vrda exp: Array double precision exponential function . . . . . . . . . vrs4 expf: Four-valued single precision exponential function . . . . vrs8 expf: Eight-valued single precision exponential function . . . vrsa expf: Array single precision exponential function . . . . . . . . . . vrd2 log: Two-valued double precision natural logarithm . . . . . . . vrd4 log: Four-valued double precision natural logarithm . . . . . . . vrda log: Array double precision natural logarithm . . . . . . . . . . . . vrs4 logf: Four-valued single precision natural logarithm . . . . . . . vrs8 logf: Eight-valued single precision natural logarithm . . . . . . vrsa logf: Array single precision natural logarithm . . . . . . . . . . . . . vrd2 log10: Two-valued double precision base-10 logarithm . . . . . vrd4 log10: Four-valued double precision base-10 logarithm . . . . vrda log10: Array double precision base-10 logarithm . . . . . . . . . . vrs4 log10f: Four-valued single precision base-10 logarithm . . . . . vrs8 log10f: Eight-valued single precision base-10 logarithm . . . . vrsa log10f: Array single precision base-10 logarithm. . . . . . . . . . . vrd2 log2: Two-valued double precision base-2 logarithm . . . . . . . vrd4 log2: Four-valued double precision base-2 logarithm . . . . . . vrda log2: Array double precision base-2 logarithm . . . . . . . . . . . . vrs4 log2f: Four-valued single precision base-2 logarithm . . . . . . . vrs8 log2f: Eight-valued single precision base-2 logarithm . . . . . . vrsa log2f: Array single precision base-2 logarithm . . . . . . . . . . . . . vrs4 powf: Four-valued single precision power function . . . . . . . . . vrsa powf: Array single precision power function . . . . . . . . . . . . . . vrs4 powxf: Four-valued single precision power function with constant y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa powxf: Array single precision power function, constant y . . vrd2 sin: Two-valued double precision Sine . . . . . . . . . . . . . . . . . . . vrd4 sin: Four-valued double precision Sine . . . . . . . . . . . . . . . . . . . vrda sin: Array double precision Sine . . . . . . . . . . . . . . . . . . . . . . . . . vrs4 sinf: Four-valued single precision Sine . . . . . . . . . . . . . . . . . . . . vrsa sinf: Array single precision Sine . . . . . . . . . . . . . . . . . . . . . . . . . vrd2 sincos: Two-valued double precision Sine and Cosine . . . . . . vrda sincos: Array double precision Sine and Cosine . . . . . . . . . . . vrs4 sincosf: Four-valued single precision Sine and Cosine . . . . . . vrsa sincosf: Array single precision Sine and Cosine . . . . . . . . . . . . 8 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 215 217 219 220 221 222 223 224 225 227 228 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Routine Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Chapter 1: Introduction 1 1 Introduction The AMD Core Math Library (ACML) is a set of numerical routines tuned specifically for AMD64 platform processors (including OpteronTM and AthlonTM 64 ). The routines, which are available via both FORTRAN 77 and C interfaces, include: • BLAS - Basic Linear Algebra Subprograms (including Sparse Level 1 BLAS); • LAPACK - A comprehensive package of higher level linear algebra routines; • FFT - a set of Fast Fourier Transform routines for real and complex data; • RNG - a set of random number generators and statistical distribution functions. The BLAS and LAPACK routines provide a portable and standard set of interfaces for common numerical linear algebra operations that allow code containing calls to these routines to be readily ported across platforms. Full documentation for the BLAS and LAPACK are available online. This manual will, therefore, be restricted to providing brief descriptions of the BLAS and LAPACK and providing links to their documentation and other materials (see Chapter 3 [The BLAS], page 19 and see Chapter 4 [LAPACK], page 20). The FFT is an implementation of the Discrete Fourier Transform (DFT) that makes use of symmetries in the definition to reduce the number of operations required from O(n*n) to O(n*log n) when the sequence length, n, is the product of small prime factors; in particular, when n is a power of 2. Despite the popularity and widespread use of FFT algorithms, the definition of the DFT is not sufficiently precise to prescribe either the forward and backward directions (these are sometimes interchanged), or the scaling factor associated with the forward and backward transforms (the combined forward and backward transforms may only reproduce the original sequence by following a prescribed scaling). Currently, there is no agreed standard API for FFT routines. Hardware vendors usually provide a set of high performance FFTs optimized for their systems: no two vendors employ the same interfaces for their FFT routines. The ACML provides a set of FFT routines, optimized for AMD64 processors, using an ACML-specific set of interfaces. The functionality, interfaces and use of the ACML FFT routines are described below (see Chapter 5 [Fast Fourier Transforms], page 24). The RNG is a comprehensive set of statistical distribution functions which are founded on various underlying uniform distribution generators (base generators) including WichmannHill and an implementation of the Mersenne Twister. In addition there are hooks which allow you to supply your own preferred base generator if it is not already included in ACML. All RNG functionality and interfaces are described below (see Chapter 6 [Random Number Generators], page 75). Chapter 2 [General Information], page 2 provides details on: • how to link a user program to the ACML; • FORTRAN and C interfaces to ACML routines; • how to obtain the ACML version and build information; • how to access the ACML documentation. A supplementary library of fast math and fast vector math functions (ACML MV) is also provided with some 64-bit versions of ACML. Some of the functions included in ACML MV are not callable from high-level languages, but must be called via assembly language; the documentation of ACML MV (see Chapter 7 [Fast Vector Math Library], page 163) gives details for each individual routine. Chapter 2: General Information 2 2 General Information 2.1 Determining the best ACML version for your system ACML comes in versions for 64-bit and 32-bit processors, running both Linux and Microsoft R Windows operating systems. To use the following tables, you will need to know answers to these questions: • Are you running a 64-bit operating system (on AMD64 hardware such as Opteron or Athlon64)? Or are you running a 32-bit operating system? • Is the operating system Linux or Microsoft Windows? • Do you have the GNU compilers (gfortran/gcc) or compatible compilers (compilers such as Absoft that are interoperable with the GNU compilers) installed? • Do you have the PGI compilers (pgf77/pgf90/pgcc) installed? • Do you have the PathScale compilers (pathf90/pathcc) installed? • Do you have the NAGWare compiler (f95) installed? • On a 32-bit Windows machine, do you have Microsoft C, or PGI Visual Fortran, or Intel Fortran, or compatible compilers installed? • Do you have a single processor system or a multiprocessor (SMP) system? The single processor version of ACML can be run on an SMP machine and vice versa, but (if you have the right compilers) it is more efficient to run the version appropriate to the machine. • If you’re on a 32-bit machine, does it support Streaming SIMD Extension instructions (SSE and SSE2)? The ACML installation includes a binary utility that can help you find an answer to the last question. The utility lies in directory util, and is named cpuid.exe. It interrogates the processor to determine whether SSE and SSE2 instructions exist. util/cpuid.exe Under a Linux operating system, another way of finding out the answer to the last question is to look at the special file /proc/cpuinfo, and see what appears under the “flags” label. Try this command: cat /proc/cpuinfo | grep flags If the list of flags includes the flag “sse” then your machine supports SSE instructions. If it also includes “sse2” then your machine supports SSE2 instructions. If your machine supports these instructions, it is better to use a version of ACML which was built to take advantage of them, for reasons of good performance. The method of examining /proc/cpuinfo can also be used under Microsoft Windows if you have the Cygwin UNIX-like tools installed (see http://www.cygwin.com/) and run a bash shell. Note that AMD64 machines always support both SSE and SSE2 instructions, under both Linux and Windows. Older (32-bit) AMD chips may support SSE but not SSE2, or neither SSE nor SSE2 instructions. Other manufacturers’ hardware may or may not support SSE or SSE2. If you link to a version of ACML that was built to use SSE or SSE2 instructions, and your machine does not in fact support them, it is likely that your program will halt due Chapter 2: General Information 3 to encountering an “illegal instruction” - you may or may not be notified of this by the operating system. For 32-bit machines, older versions of ACML (ACML 3.1.0 and earlier) came in variants suitable for hardware without SSE/SSE2 instructions (Streaming SIMD Extensions). This is no longer the case, and if you have older 32-bit hardware that does not support SSE/SSE2, and wish to use ACML, you must continue to use an older version. Once you have answered the questions above, use these tables to decide which version of ACML to link against. Linux 64-bit Number of processors Single processor ” ” ” ” ” Multi processor or core ” ” ” ” Compilers PGI pgf77/pgf90/pgcc GNU gfortran/gcc or compat. PathScale pathf90/pathcc Intel Fortran NAGWare f95 Absoft (use gfortran ACML) PGI pgf77/pgf90/pgcc GNU gfortran/gcc or compat. PathScale pathf90/pathcc Intel Fortran Absoft (use gfortran ACML) ACML install directory acml4.2.0/pgi64 acml4.2.0/gfortran64 acml4.2.0/pathscale64 acml4.2.0/ifort64 acml4.2.0/nag64 acml4.2.0/gfortran64 acml4.2.0/pgi64_mp acml4.2.0/gfortran64_mp acml4.2.0/pathscale64_mp acml4.2.0/ifort64_mp acml4.2.0/gfortran64_mp Linux 32-bit Number of processors Single processor ” ” ” ” ” Multi processor or core ” ” ” ” Compilers PGI pgf77 / pgf90 / pgcc GNU gfortran / gcc or compat. PathScale pathf90 / pathcc Intel Fortran NAGWare f95 Absoft (use gfortran ACML) PGI pgf77 / pgf90 / pgcc GNU gfortran / gcc or compat. PathScale pathf90 / pathcc Intel Fortran Absoft (use gfortran ACML) ACML install directory acml4.2.0/pgi32 acml4.2.0/gfortran32 acml4.2.0/pathscale32 acml4.2.0/ifort32 acml4.2.0/nag32 acml4.2.0/gfortran32 acml4.2.0/pgi32_mp acml4.2.0/gfortran32_mp acml4.2.0/pathscale32_mp acml4.2.0/ifort32_mp acml4.2.0/gfortran32_mp Chapter 2: General Information 4 Microsoft Windows 64-bit Number of processors Single processor ” Multi processor or core ” Compilers PGI pgf77/pgf90/pgcc/MSC Intel Fortran/Microsoft C PGI pgf77/pgf90/pgcc/MSC Intel Fortran/Microsoft C ACML install directory acml4.2.0/win64 acml4.2.0/ifort64 acml4.2.0/win64_mp acml4.2.0/ifort64_mp Microsoft Windows 32-bit Number of processors Single processor ” Multi processor or core ” Compilers PGI pgf77/pgf90/Microsoft C Intel Fortran/Microsoft C PGI pgf77/pgf90/Microsoft C Intel Fortran/Microsoft C ACML install directory acml4.2.0/pgi32 acml4.2.0/ifort32 acml4.2.0/pgi32_mp acml4.2.0/ifort32_mp 2.2 Accessing the Library (Linux) 2.2.1 Accessing the Library under Linux using GNU gfortran/gcc If the Linux 64-bit gfortran version of ACML was installed in the default directory, /opt/acml4.2.0/gfortran64, then the command: gfortran -m64 driver.f -L/opt/acml4.2.0/gfortran64/lib -lacml can be used to compile the program driver.f and link it to the ACML. The ACML Library is supplied in both static and shareable versions, libacml.a and libacml.so, respectively. By default, the commands given above will link to the shareable version of the library, libacml.so, if that exists in the directory specified. Linking with the static library can be forced either by using the compiler flag -static, e.g. gfortran -m64 driver.f -L/opt/acml4.2.0/gfortran64/lib -static -lacml or by inserting the name of the static library explicitly in the command line, e.g. gfortran -m64 driver.f /opt/acml4.2.0/gfortran64/lib/libacml.a Notice that if the application program has been linked to the shareable ACML Library, then before running the program, the environment variable LD_LIBRARY_PATH must be set. Assuming that libacml.so was installed in the directory /opt/acml4.2.0/gfortran64/lib, then LD_LIBRARY_PATH may be set by, for example, the C-shell command setenv LD_LIBRARY_PATH /opt/acml4.2.0/gfortran64/lib (See the man page for ld(1) for more information about LD_LIBRARY_PATH.) The command gfortran -m32 driver.f -L/opt/acml4.2.0/gfortran32/lib -lacml will compile and link a 32-bit program with a 32-bit ACML. If you have an SMP machine and want to take best advantage of it, link against the gfortran OpenMP version of ACML like this: Chapter 2: General Information 5 gfortran -fopenmp -m64 driver.f -L/opt/acml4.2.0/gfortran64_mp/lib -lacml_mp gfortran -fopenmp -m32 driver.f -L/opt/acml4.2.0/gfortran32_mp/lib -lacml_mp Note that the directories and library names involved now include the suffix mp. To compile and link a 64-bit C program with a 64-bit ACML, invoke gcc -m64 -I/opt/acml4.2.0/gfortran64/include driver.c -L/opt/acml4.2.0/gfortran64/lib -lacml -lgfortran The switch "-I/opt/acml4.2.0/gfortran64/include" tells the compiler to search the directory /opt/acml4.2.0/gfortran64/include for the ACML C header file acml.h, which should be included by driver.c. Note that it is necessary to add the gfortran compiler run-time library -lgfortran when linking the program. 2.2.2 Accessing the Library under Linux using PGI compilers pgf77/pgf90/pgcc Similar commands apply for the PGI versions of ACML. For example, pgf77 -tp=k8-64 -Mcache_align driver.f -L/opt/acml4.2.0/pgi64/lib -lacml pgf77 -tp=k8-32 -Mcache_align driver.f -L/opt/acml4.2.0/pgi32/lib -lacml will compile driver.f and link it to the ACML using 64-bit and 32-bit versions respectively. In the example above we are linking with the single-processor PGI version of ACML. If you have an SMP machine and want to take best advantage of it, link against the PGI OpenMP version of ACML like this: pgf77 -tp=k8-64 -mp -Mcache_align driver.f -L/opt/acml4.2.0/pgi64_mp/lib -lacml_mp pgf77 -tp=k8-32 -mp -Mcache_align driver.f -L/opt/acml4.2.0/pgi32_mp/lib -lacml_mp Note that the directories and library names involved now include the suffix mp. The -mp flag is important - it tells pgf77 to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. The -Mcache align flag is also important - it tells the compiler to align objects on cache-line boundaries. The commands pgcc -c -tp=k8-64 -mp -Mcache_align -I/opt/acml4.2.0/pgi64_mp/include driver.c pgcc -tp=k8-64 -mp -Mcache_align driver.o -L/opt/acml4.2.0/pgi64_mp/lib -lacml_mp -lpgftnrtl -lm will compile driver.c and link it to the 64-bit ACML. Again, the -mp flag is important if you are linking to the PGI OpenMP version of ACML. The C compiler is instructed to search the directory /opt/acml4.2.0/pgi64 mp/include for the ACML C header file acml.h, which should be included by driver.c, by using the switch "-I/opt/acml4.2.0/pgi64 mp/include". Note that in the example we add the libraries -lpgftnrtl and -lm to the link command, so that required PGI compiler run-time libraries are found. Note that since ACML version 3.5.0, all PGI 64-bit variants are compiled with the PGI -Mlarge arrays switch to allow use of larger data arrays (see PGI compiler documentation for more information). The special ’large array’ variants that were distributed with earlier versions of ACML are therefore no longer required. Chapter 2: General Information 6 2.2.3 Accessing the Library under Linux using PathScale compilers pathf90/pathcc Similar commands apply for the PathScale versions of ACML. For example, pathf90 driver.f -L/opt/acml4.2.0/pathscale64/lib -lacml will compile driver.f and link it to the ACML using the 64-bit version. The commands pathcc -c -I/opt/acml4.2.0/pathscale64/include driver.c pathcc driver.o -L/opt/acml4.2.0/pathscale64/lib -lacml -lpathfortran will compile driver.c and link it to the 64-bit ACML. The switch -I/opt/acml4.2.0/pathscale64/include tells the C compiler to search directory /opt/acml4.2.0/pathscale64/include for the ACML C header file acml.h, which should be included by driver.c. Note that in the example we add the library -lpathfortran to the link command, so that the required PathScale compiler run-time library is found. If you have an SMP machine and want to take best advantage of it, link against the PathScale OpenMP version of ACML like this: pathf90 -mp driver.f -L/opt/acml4.2.0/pathscale64_mp/lib -lacml_mp pathf90 -mp driver.f -L/opt/acml4.2.0/pathscale32_mp/lib -lacml_mp Note that the directories and library names involved now include the suffix mp. The -mp flag is important - it tells pathf90 to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. The commands pathcc -c -mp -I/opt/acml4.2.0/pathscale64_mp/include driver.c pathcc -mp driver.o -L/opt/acml4.2.0/pathscale64_mp/lib -lacml_mp -lpathfortran will compile driver.c and link it to the 64-bit ACML. Again, the -mp flag is important if you are linking to the PathScale OpenMP version of ACML. The C compiler is instructed to search the directory /opt/acml4.2.0/pathscale64 mp/include for the ACML C header file acml.h, which should be included by driver.c, by using the switch "-I/opt/acml4.2.0/pathscale64 mp/include". Note that in the example we add the library -lpathfortran to the link command, so that a required PathScale compiler run-time library is found. 2.2.4 Accessing the Library under Linux using the NAGWare f95 compiler Similar commands apply for the NAGware f95 versions of ACML. For example, f95 driver.f -L/opt/acml4.2.0/nag64/lib -lacml f95 -32 driver.f -L/opt/acml4.2.0/nag32/lib -lacml will compile driver.f and link it to the ACML using the 64-bit version and 32-bit version respectively. Chapter 2: General Information 7 2.2.5 Accessing the Library under Linux using the Intel ifort compiler Similar commands apply for the Intel ifort versions of ACML. For example, ifort driver.f -L/opt/acml4.2.0/ifort64/lib -lacml will compile driver.f and link it to the ACML using the 64-bit version. The commands gcc -c -I/opt/acml4.2.0/ifort64/include driver.c ifort -nofor-main driver.o -L/opt/acml4.2.0/ifort64/lib -lacml will compile driver.c and link it to the 64-bit ACML. The switch -I/opt/acml4.2.0/ifort64/include tells the C compiler to search directory /opt/acml4.2.0/ifort64/include for the ACML C header file acml.h, which should be included by driver.c. Note that in the example we link the C program using the ifort compiler with the -nofor-main switch, so that required ifort compiler run-time libraries are found. If you have an SMP machine and want to take best advantage of it, link against the ifort OpenMP version of ACML like this: ifort -openmp driver.f -L/opt/acml4.2.0/ifort64_mp/lib -lacml_mp ifort -openmp driver.f -L/opt/acml4.2.0/ifort32_mp/lib -lacml_mp Note that the directories and library names involved now include the suffix mp. The -openmp flag is important - it tells ifort to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. 2.2.6 Accessing the Library under Linux using Absoft af90 The Absoft compiler af90 is compatible with the GNU compiler gfortran version of ACML, so long as the appropriate gfortran run-time libraries are installed on your system. If the Linux 64-bit gfortran version of ACML was installed in the default directory, /opt/acml4.2.0/gfortran64, then the command: af90 -m64 driver.f -L/opt/acml4.2.0/gfortran64/lib -lacml -lgfortran can be used to compile the program driver.f and link it to the ACML. Note that -gfortran links to the gfortran run-time library, which must be installed on your system. The ACML Library is supplied in both static and shareable versions, libacml.a and libacml.so, respectively. By default, the commands given above will link to the shareable version of the library, libacml.so, if that exists in the directory specified. Linking with the static library can be forced either by using the compiler flag -static, e.g. af90 -m64 driver.f -L/opt/acml4.2.0/gfortran64/lib -static \ -lacml -lgfortran or by inserting the name of the static library explicitly in the command line, e.g. af90 -m64 driver.f /opt/acml4.2.0/gfortran64/lib/libacml.a -lgfortran Notice that if the application program has been linked to the shareable ACML Library, then before running the program, the environment variable LD_LIBRARY_PATH must be set. Assuming that libacml.so was installed in the directory /opt/acml4.2.0/gfortran64/lib, then LD_LIBRARY_PATH may be set by, for example, the C-shell command Chapter 2: General Information 8 setenv LD_LIBRARY_PATH /opt/acml4.2.0/gfortran64/lib (See the man page for ld(1) for more information about LD_LIBRARY_PATH.) The command af90 -m32 driver.f -L/opt/acml4.2.0/gfortran32/lib -lacml -lgfortran will compile and link a 32-bit program with a 32-bit ACML. If you have an SMP machine and want to take best advantage of it, link against the gfortran OpenMP version of ACML like this: af90 -m64 driver.f -L/opt/acml4.2.0/gfortran64_mp/lib -lacml_mp -lgfortran -lgomp af90 -m32 driver.f -L/opt/acml4.2.0/gfortran32_mp/lib -lacml_mp -lgfortran -lgomp Note that the directories and library names involved now include the suffix mp. Also note that it is necessary to link to the gfortran run-time libraries -lgfortran -lgomp, both of which must be installed on your system. 2.2.7 Accessing the Library under Linux using compilers other than GNU, PGI, PathScale, NAGWare, Intel or Absoft It may be possible to link to some versions of ACML using compilers other than those already mentioned, if they are compatible with one of the other versions. If you do this, it may be necessary to link to the run-time library of the compiler used to build the ACML you link to, in order to satisfy run-time symbols. Since doing this is very compiler-specific, we give no further details here. 2.3 Accessing the Library (Microsoft Windows) 2.3.1 Accessing the Library under 32-bit Windows using PGI compilers pgf77/pgf90/Microsoft C To use the 32-bit Windows PGI version of ACML, use a command like pgf77 -Mdll -Munix driver.f "c:\Program Files\AMD\acml4.2.0\pgi32\lib\libacml_dll.lib" where libacml dll.lib is the import library for the ACML DLL. Note that it is important to use the compiler switch -Munix in order to tell the compiler to use the same calling convention as was used to build ACML. In the example above we are linking with the single-processor PGI version of ACML. If you have an SMP machine and want to take best advantage of it, link against the PGI OpenMP version of ACML like this: pgf77 -Mdll -Munix -mp driver.f "c:\Program Files\AMD\acml4.2.0\pgi32_mp\lib\libacml_mp_dll.lib" Note that the directories and library names involved now include the suffix mp. For the OpenMP version of ACML, if you link to the static library libacml mp.lib rather than the DLL import library libacml mp dll.lib, you will need to use the PGI compiler flag -mp in order to tell the compiler to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. This should not be necessary when linking to the ACML DLL because the DLL itself knows that it depends on the run-time library; but using the -mp flag in any case will do no harm. Chapter 2: General Information 9 To compile and link a C program using the Microsoft C command line compiler, cl, the commands cl "-Ic:\Program Files\AMD\acml4.2.0\pgi32\include" /MD driver.c "c:\Program Files\AMD\acml4.2.0\pgi32\lib\libacml_dll.lib" cl "-Ic:\Program Files\AMD\acml4.2.0\pgi32_mp\include" /MD driver.c "c:\Program Files\AMD\acml4.2.0\pgi32_mp\lib\libacml_mp_dll.lib" will link against the single-threaded DLL and multi-threaded versions of ACML respectively. 2.3.2 Accessing the Library under 32-bit Windows using Microsoft C or Intel Fortran To use the 32-bit Windows MSC/Intel Fortran version of ACML, use a command like ifort /threads /libs:dll driver.f "c:\Program Files\AMD\acml4.2.0\ifort32\lib\libacml_dll.lib" where libacml dll.lib is the import library for the ACML DLL. In the example above we are linking with the single-processor ifort version of ACML. If you have an SMP machine and want to take best advantage of it, link against the ifort OpenMP version of ACML like this: ifort /libs:dll -Qopenmp driver.f c:\acml4.2.0\ifort32_mp\lib\libacml_mp_dll.lib Note that the directories and library names involved now include the suffix mp. For the OpenMP version of ACML, if you link to the static library libacml mp.lib rather than the DLL import library libacml mp dll.lib, you will need to use the ifort compiler flag -Qopenmp in order to tell the compiler to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. This should not be necessary when linking to the ACML DLL because the DLL itself knows that it depends on the run-time library; but using the -Qopenmp flag in any case will do no harm. To compile and link a C program using the Microsoft C command line compiler, cl, the commands cl "-Ic:\Program Files\AMD\acml4.2.0\ifort32\include" /MD driver.c "c:\Program Files\AMD\acml4.2.0\ifort32\lib\libacml_dll.lib" cl "-Ic:\Program Files\AMD\acml4.2.0\ifort32_mp\include" /MD driver.c "c:\Program Files\AMD\acml4.2.0\ifort32_mp\lib\libacml_mp_dll.lib" will link against the single-threaded DLL and multi-threaded versions of ACML respectively. ACML can also be linked from inside a development environment such as Microsoft Visual Studio or Visual Studio.NET. Again, it is important to get compilation options correct. The directory acml4.2.0\ifort32\examples\Projects contains a few sample Visual Studio project directories showing how this can be done. Chapter 2: General Information 10 Note that in both examples above we linked to a DLL version of ACML, and so before running the resulting programs the environment variable PATH must be set to include the location of the DLL. For example, assuming that libacml dll.dll was installed in "c:\Program Files\AMD\acml4.2.0\ifort32\lib", PATH may be set by, for example, the DOS command PATH="c:\Program Files\AMD\acml4.2.0\ifort32\lib";%PATH% Alternatively, the PATH environment variable may be set in the system category of the Windows control panel. ACML also comes as a static (non-DLL) library, named libacml.lib, in the same directory as the DLL. If you link to the static library instead of the DLL import library then there is no need to set the PATH. 2.3.3 Accessing the Library under 32-bit Windows using the Compaq Visual Fortran compiler The win32 Intel Fortran variant of ACML can be used with the Compaq Visual Fortran compiler as follows: f90 /iface:cref,nomixed_str_len_arg /threads /libs:dll driver.f "c:\Program Files\AMD\acml4.2.0\ifort32\lib\libacml_dll.lib" where f90 is the Compaq Visual Fortran command line compiler and libacml dll.lib is the import library for the ACML DLL. The switch /iface:cref,nomixed str len arg used on the f90 compiler command line is important - it tells the compiler to use a calling convention equivalent to the default Intel Fortran calling convention, rather than the default cvf stdcall calling convention. If you forget to use this switch your program is likely to crash on execution. 2.3.4 Accessing the Library under 32-bit Windows using the Salford FTN95 compiler The win32 Intel Fortran variant of ACML can be used with the Salford ftn95 compiler as follows: ftn95 driver.f The resulting object file can be linked using the Salford linker, slink, for example like this: slink driver.obj install_dir\libacml_dll.dll where install dir is the location of the DLL. The full pathname of install dir should be specified to the DLL and should be enclosed within quotes if it contains spaces. It is worth emphasising that the linker should link directly against the DLL itself, not the libacml dll.lib import library. Chapter 2: General Information 11 2.3.5 Accessing the Library under 64-bit Windows using PGI compilers pgf77/pgf90/pgcc Under 64-bit versions of Windows, ACML 4.2.0 comes as a static (.LIB) library or a DLL. To link with the 64-bit Windows DLL library PGI version of ACML, in a DOS command prompt use a command like pgf77 -Mdll driver.f c:/acml4.2.0/win64/lib/libacml_dll.lib where libacml dll.lib is the import library for the DLL. In the example above we are linking with the single-processor WIN64 version of ACML. If you have an SMP machine and want to take best advantage of it, link against the WIN64 OpenMP version of ACML like this: pgf77 -Mdll -mp driver.f c:/acml4.2.0/win64_mp/lib/libacml_mp_dll.lib Note that the directories and library names involved now include the suffix mp. For the OpenMP version of ACML, if you link to the static library libacml mp.lib rather than the DLL import library libacml mp dll.lib, you will need to use the PGI compiler flag -mp in order to tell the compiler to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. This should not be necessary when linking to the ACML DLL because the DLL itself knows that it depends on the run-time library; but using the -mp flag in any case will do no harm. Note that the performance of OpenMP code produced with the PGI WIN64 compilers depends on environment variables named MP BIND and MP SPIN, which control how multiple threads behave (see PGI compiler documentation for discussion of these variables). For ACML, empirical experiments show that higher values of MP SPIN than the default are likely to give better performance. We recommend that users set MP BIND=yes and MP SPIN=100000000. Under WIN64, to compile and link a C program, the commands pgcc -Mdll driver.c -Ic:/acml4.2.0/win64/include c:/acml4.2.0/win64/lib/libacml_dll.lib pgcc -Mdll -mp driver.c -Ic:/acml4.2.0/win64_mp/include c:/acml4.2.0/win64_mp/lib/libacml_mp_dll.lib will link against the single-threaded DLL and multi-threaded versions of ACML respectively. To use the Microsoft C command line compiler, cl, use commands like this: cl driver.c -Ic:/acml4.2.0/win64/include c:/acml4.2.0/win64/lib/libacml_dll.lib cl driver.c -Ic:/acml4.2.0/win64_mp/include c:/acml4.2.0/win64_mp/lib/libacml_mp_dll.lib for single- and multi-threaded ACML variants respectively. Chapter 2: General Information 12 2.3.6 Accessing the Library under 64-bit Windows using Microsoft C or Intel Fortran Under 64-bit versions of Windows, ACML 4.2.0 comes as a static (.LIB) library or a DLL. To link with the 64-bit Windows DLL library Intel Fortran version of ACML, in a DOS command prompt use a command like ifort /libs:dll driver.f c:\acml4.2.0\ifort64\lib\libacml_dll.lib where libacml dll.lib is the import library for the DLL. In the example above we are linking with the single-processor ifort version of ACML. If you have an SMP machine and want to take best advantage of it, link against the ifort OpenMP version of ACML like this: ifort /libs:dll -Qopenmp driver.f c:\acml4.2.0\win64_mp\lib\libacml_mp_dll.lib Note that the directories and library names involved now include the suffix mp. For the OpenMP version of ACML, if you link to the static library libacml mp.lib rather than the DLL import library libacml mp dll.lib, you will need to use the ifort compiler flag -Qopenmp in order to tell the compiler to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. This should not be necessary when linking to the ACML DLL because the DLL itself knows that it depends on the run-time library; but using the -Qopenmp flag in any case will do no harm. Under WIN64, to compile and link a C program using the Microsoft C command line compiler, cl, the commands cl driver.c -Ic:/acml4.2.0/ifort64/include c:/acml4.2.0/ifort64/lib/libacml_dll.lib cl driver.c -Ic:/acml4.2.0/ifort64_mp/include c:/acml4.2.0/ifort64_mp/lib/libacml_mp_dll.lib will link against the single-threaded DLL and multi-threaded versions of ACML respectively. 2.4 Accessing the Library (Solaris) 2.4.1 Accessing the Library under Solaris If the Solaris 64-bit f95 version of ACML was installed in the default directory, /opt/acml4.2.0/sun64, then the command: f95 -xarch=amd64 driver.f -L/opt/acml4.2.0/sun64/lib -lacml can be used to compile the program driver.f and link it to the ACML. The ACML Library is supplied in both static and shareable versions, libacml.a and libacml.so, respectively. By default, the commands given above will link to the shareable version of the library, libacml.so, if that exists in the directory specified. Linking with the static library can be forced either by using the compiler flag -Bstatic, e.g. f95 -xarch=amd64 driver.f -L/opt/acml4.2.0/sun64/lib -Bstatic -lacml or by inserting the name of the static library explicitly in the command line, e.g. Chapter 2: General Information 13 f95 -xarch=amd64 driver.f /opt/acml4.2.0/sun64/lib/libacml.a Notice that if the application program has been linked to the shareable ACML Library, then before running the program, the environment variable LD_LIBRARY_PATH must be set, for example, by the C-shell command: setenv LD_LIBRARY_PATH /opt/acml4.2.0/sun64/lib where it is assumed that libacml.so was installed in the directory /opt/acml4.2.0/sun64/lib (see the man page for ld(1) for more information about LD_LIBRARY_PATH.). The command f95 -xarch=sse2 driver.f -L/opt/acml4.2.0/sun32/lib -lacml will compile and link a 32-bit program with a 32-bit ACML. To compile and link a 64-bit C program with a 64-bit ACML, invoke cc -xarch=amd64 -I/opt/acml4.2.0/sun64/include driver.c -L/opt/acml4.2.0/sun64/lib -lacml -lfsu -lsunmath -lm The switch "-I/opt/acml4.2.0/sun64/include" tells the compiler to search the directory /opt/acml4.2.0/sun64/include for the ACML C header file acml.h, which should be included by driver.c. Note that it is necessary to add the Sun compiler run-time libraries -lfsu -lsunmath -lm when linking the program. If you have an SMP machine and want to take best advantage of it, link against the Solaris OpenMP version of ACML like this: f95 -openmp -xarch=amd64 driver.f -L/opt/acml4.2.0/sun64_mp/lib -lacml_mp f95 -openmp -xarch=sse2 driver.f -L/opt/acml4.2.0/sun32_mp/lib -lacml_mp Note that the directories and library names involved now include the suffix mp. The -openmp flag is important - it tells f95 to link with the appropriate compiler OpenMP run-time library. Without it you might get an "unresolved symbol" message at link time. The command cc -openmp -xarch=amd64 -I/opt/acml4.2.0/sun64/include driver.c -L/opt/acml4.2.0/sun64/lib -lacml_mp -lfsu -lsunmath -lm -lmtsk will compile driver.c and link it to the 64-bit ACML. Again, the -openmp flag is important if you are linking to the OpenMP version of ACML. The C compiler is instructed to search the directory /opt/acml4.2.0/sun64 mp/include for the ACML C header file acml.h, which should be included by driver.c, by using the switch "-I/opt/acml4.2.0/sun64 mp/include". Note that in the example we add the libraries -lfsu -lsunmath -lm -lmtsk to the link command, so that required compiler run-time libraries are found. Chapter 2: General Information 14 2.5 ACML FORTRAN and C interfaces All routines in ACML come with both FORTRAN and C interfaces. The FORTRAN interfaces typically follow the relevant standard (e.g. LAPACK, BLAS). Here we document how a C programmer should call ACML routines. In C code that uses ACML routines, be sure to include the header file <acml.h>, which contains function prototypes for all ACML C interfaces. The header file also contains C prototypes for FORTRAN interfaces, thus the C programmer could call the FORTRAN interfaces from C, though there is little reason to do so. C interfaces to ACML routines differ from FORTRAN interfaces in the following major respects: • The FORTRAN interface names are appended by an underscore (except for the Windows 32-bit and 64-bit Microsoft C/Intel Fortran version of ACML, where FORTRAN interface names are distinguished from C by being upper case rather than lower case this is the default for the Intel Fortran compiler) • The C interfaces contain no workspace arguments; all workspace memory is allocated internally. • Scalar input arguments are passed by value in C interfaces. FORTRAN interfaces pass all arguments (except for character string length arguments that are normally hidden from FORTRAN programmers) by reference. • Most arguments that are passed as character string pointers to FORTRAN interfaces are passed by value as single characters to C interfaces. The character string length arguments of FORTRAN interfaces are not required in the C interfaces. • Unlike FORTRAN, C has no native complex data type. ACML C routines which operate on complex data use the types complex and doublecomplex defined in <acml.h> for single and double precision computations respectively. Some of the programs in the ACML examples directory (see Section 2.9 [Examples], page 17) make use of these types. It is important to note that in both the FORTRAN and C interfaces, 2-dimensional arrays are assumed to be stored in column-major order. e.g. the matrix A= 1.0 2.0 3.0 4.0 would be stored in memory as 1.0, 3.0, 2.0, 4.0. This storage order corresponds to a FORTRAN-style 2-D array declaration A(2,2), but not to an array declared as a[2][2] in C which would be stored in row-major order as 1.0, 2.0, 3.0, 4.0. As an example, compare the FORTRAN and C interfaces of LAPACK routine dsytrf as implemented in ACML. FORTRAN: void dsytrf_(char *uplo, int *n, double *a, int *lda, int *ipiv, double *work, int *lwork, int *info, int uplo_len); C: void dsytrf(char uplo, int n, double *a, int lda, int *ipiv, int *info); C code calling both the above variants might look like this: Chapter 2: General Information 15 double *a; int *ipiv; double *work; int n, lda, lwork, info; /* Assume that all arrays and variables are allocated and initialized as required by dsytrf. */ /* Call the FORTRAN version of dsytrf. The first argument is a character string, and the last argument is the length of that string. The input scalar arguments n, lda and lwork, as well as the output scalar argument info, are all passed by reference. */ dsytrf_("Upper", &n, a, &lda, ipiv, work, &lwork, &info, 5); /* Call the C version of dsytrf. The first argument is a character, workspace is not required, and input scalar arguments n and lda are passed by value. Output scalar argument info is passed by reference. */ dsytrf(’U’, n, a, lda, ipiv, &info); 2.6 ACML variants using 64-bit integer (INTEGER*8) arguments Where compilers support, through the use of switches, the automatic promotion of regular INTEGER (32-bit) arguments to INTEGER*8 (64-bit) arguments, ACML variants exist to use this facility. This means that if you have a 64-bit Fortran program using INTEGER*8 variables, or a 64-bit C program using 8-byte long variables, there is an ACML version that you can use. This applies to 64-bit ACML versions built with PGI, PathScale, gfortran, Intel and NAG compilers. The INTEGER*8 versions of these libraries are distinguished from the usual versions by having the string “ int64” as part of the name of the directory under which ACML is installed. Thus, for example, if the regular PGI 64-bit library is in a directory named pgi64, then the INTEGER*8 version will be installed in directory pgi64 int64. For these ACML variants, all ACML documentation that mentions arguments of Fortran type INTEGER or C type int should be read as INTEGER*8 or long respectively. It is important to ensure that if you have INTEGER*8 variables in your code, you link to the int64 variant, and not otherwise. Unexpected program crashes are likely to occur if you link to the wrong version. Chapter 2: General Information 16 2.7 Library Version and Build Information This document is applicable to version 4.2.0 of ACML. The utility routine acmlversion can be called to obtain the major, minor and patch version numbers of the installed ACML. This routine returns three integers; the major, minor and patch version numbers, respectively. The utility routine acmlinfo can be called to obtain information on the compiler used to build ACML, the version of the compiler, and the options used for building the Library. This subroutine takes no arguments and prints the information to the current standard output. FORTRAN specifications: ACMLVERSION (MAJOR, MINOR, PATCH ) MAJOR, MINOR, PATCH ACMLINFO () [SUBROUTINE] [INTEGER] [SUBROUTINE] C specifications: void acmlversion (int *major, int *minor, int *patch ); [function] void acmlinfo (void); [function] 2.8 Library Documentation The /Doc subdirectory of the top ACML installation directory, (e.g. /opt/acml4.2.0/Doc under Linux, or c:\Program Files\AMD\acml4.2.0\Doc under Windows), should contain this document in the following formats: • Printed Manual / PDF format – acml.pdf • Info Pages – acml.info (Linux only) • Html – html/index.html • Plain text – acml.txt Under Linux the info file can be read using info after updating the environment variable INFOPATH to include the doc subdirectory of the ACML installation directory, e.g. % setenv INFOPATH ${INFOPATH}:/opt/acml4.2.0/Doc % info acml or simply by using the full name of the file: % info /opt/acml4.2.0/Doc/acml.info Chapter 2: General Information 17 2.9 Example programs calling ACML The /examples subdirectory of the top ACML installation directory (for example, possible default locations are /opt/acml4.2.0/pgi64/examples under Linux, or, under windows, c:\acml4.2.0\win64\examples), contains example programs showing how to call the ACML, along with a GNUmakefile to build and run them. Examples of calling both FORTRAN and C interfaces are included. They may be used as an ACML installation test. Depending on where your copy of the ACML is installed, and which compiler and flags you wish to use, it may be necessary to modify some variables in the GNUmakefile before using it. The 32-bit Windows versions of ACML assume that you have the Cygwin UNIX-like tools installed, and can use the make command that comes with them to build the examples. For the 64-bit Windows version of ACML, it is not necessary to have the Cygwin tools. The examples directory contains a bat script, acmlexample.bat, which can be used to run one of the example programs. Another bat script, acmlallexamples.bat, builds and runs all the examples in the directory. Alternatively, if you do have the Cygwin tools installed, you can use the GNUmakefile to build the examples. If you need more example programs showing how to call LAPACK routines from Fortran, we refer you to this web page: http://www.nag.com/lapack/ Here you will find examples for all double precision LAPACK driver routines, and all of these should work when linked with ACML. Note that as well as the example programs themselves, it is necessary to download and compile a small amount of utility code used by the programs. See the web page for detailed instructions. 2.10 Example ACML programs demonstrating performance The /examples/performance subdirectory of the top ACML installation directory (for example, possible default locations are /opt/acml4.2.0/pgi64/examples/performance under Linux, or c:\acml4.2.0\win64\examples\performance under windows) contains several timing programs designed to show the performance of ACML when running on your machine. Again, a GNUmakefile may be used to build and run them. Depending on where your copy of the ACML is installed, and which compiler and flags you wish to use, it may be necessary to modify some variables in the GNUmakefile before using it. The 32- and 64-bit Windows versions of ACML assume that you have the Cygwin UNIXlike tools installed, and can use the make command that comes with them to build the examples. In addition, the GNUmakefile uses the gnuplot plotting program to display graphs of the timing results. If you do not have gnuplot installed, the timing programs will still run and show their results, but you will see no graph plots. Under linux, gnuplot may come with your linux distribution, but you may need to explicitly ask for it to be installed. Note that version 4.0 or later of gnuplot is required. The gnuplot program is also available for Windows machines. See http://www.gnuplot.info for more information. If you are on an SMP (multiprocessor) machine and have installed an OpenMP version of the ACML, then in the examples/performance directory a command such as Chapter 2: General Information 18 % make OMP_NUM_THREADS=5 will run the timing programs on P processors, where P = 1, 2, 4, 5; i.e., P equals an integer power of 2 and also equals OMP NUM THREADS if this value is not a power of 2. The results for a particular routine are concatenated into one file. gnuplot then shows on one graph for each routine the results of varying the number of processors for that routine. Setting OMP NUM THREADS in this way is not useful if you are not on an SMP machine or are not using an OpenMP version of ACML. Neither is it useful to set OMP NUM THREADS to a value higher than the number of processors (or processor cores) on your machine. A way to find the number of processors (or cores) under linux is to examine the special file /proc/cpuinfo which has an entry for every core. Not all routines in ACML are SMP parallelized, so in this context the OMP NUM THREADS setting only applies to those examples, including time cfft2d.f90, time dgemm.f90 and time dgetrf.f90, which are for parallelized routines. The other timing programs run on one thread regardless of the setting of OMP NUM THREADS. In all cases, timing graphs can be viewed without regenerating timing results by typing the command % make plots Note that all results generated by timing programs will vary depending on the load on your machine at run time. Chapter 3: BLAS: Basic Linear Algebra Subprograms 19 3 BLAS: Basic Linear Algebra Subprograms The BLAS are a set of well defined basic linear algebra operations ([1], [2], [3]). These operations are subdivided into three groups: • Level 1: operations acting on vectors only (e.g. dot product) • Level 2: matrix-vector operations (e.g. matrix-vector multiplication) • Level 3: matrix-matrix operations (e.g. matrix-matrix multiplication) Efficient machine-specific implementations of the BLAS are available for many modern high-performance computers. The implementation of higher level linear algebra algorithms on these systems depends critically on the use of the BLAS as building blocks. AMD provides, as part of the ACML, an implementation of the BLAS optimized for performance on AMD64 processors. For any information relating to the BLAS please refer to the BLAS FAQ: http://www.netlib.org/blas/faq.html ACML also includes interfaces to the extensions to Level 1 BLAS known as the sparse BLAS. These routines perform operations on a sparse vector x which is stored in compressed form and a vector y in full storage form. See reference [4] for more information. Chapter 4: LAPACK: Package of Linear Algebra Subroutines 20 4 LAPACK: Package of Linear Algebra Subroutines 4.1 Introduction to LAPACK LAPACK ([5]) is a library of FORTRAN 77 subroutines for solving commonly occurring problems in numerical linear algebra. LAPACK components can solve systems of linear equations, linear least squares problems, eigenvalue problems and singular value problems. Dense and banded matrices are provided for, but not general sparse matrices. In all areas, similar functionality is provided for real and complex matrices. LAPACK routines are written so that as much as possible of the computations is performed by calls to the BLAS. The efficiency of LAPACK routines depends, in large part, on the efficiency of the BLAS being called. Block algorithms are employed wherever possible to maximize the use of calls to level 3 BLAS, which generally run faster than lower level BLAS due to the high number of operations per memory access. The performance of some of the LAPACK routines has been further improved by reworking the computational algorithms. Some of the LAPACK routines contained in ACML are therefore based on code that is different from the LAPACK sources available in the public domain. In all these cases the algorithmic and numerical properties of the original LAPACK routines have been strictly preserved. Furthermore, key LAPACK routines have been treated using OpenMP to take advantage of multiple processors when running on SMP machines. Your application will automatically benefit when you link with the OpenMP versions of ACML. 4.2 Reference sources for LAPACK The LAPACK homepage can be accessed on the World Wide Web via the URL address: http://www.netlib.org/lapack/ The on-line version of the Lapack User’s Guide, Third Edition ([5]) is available from this homepage, or directly using the URL: http://www.netlib.org/lapack/lug/index.html The standard source code is available for download from netlib, with separate distributions R for UNIX/Linux and Windows installations: http://www.netlib.org/lapack/lapack.tgz http://www.netlib.org/lapack/lapack-pc.zip A list of known problems, bugs, and compiler errors for LAPACK, as well as an errata list for the LAPACK User’s Guide ([5]), is maintained on netlib http://www.netlib.org/lapack/release_notes A LAPACK FAQ (Frequently Asked Questions) file can also be accessed via the LAPACK homepage http://www.netlib.org/lapack/faq.html Chapter 4: LAPACK: Package of Linear Algebra Subroutines 21 4.3 LAPACK block sizes, ILAENV and ILAENVSET As described in Section 6.2 of the LAPACK User’s Guide, block sizes and other parameters used by various LAPACK routines are returned by the LAPACK inquiry function ILAENV. In ACML, values returned by ILAENV have been chosen to achieve very good performance on a wide variety of hardware and problem sizes. In general it is unlikely that you will want or need to be concerned with these parameters. However, in some cases it may be that a default value returned by ILAENV is not optimal for your particular hardware and problem size. Following the advice in the LAPACK User’s Guide may enable you to choose a better value in some circumstances. For convenience, ACML includes a subroutine which allows you to override default values returned by ILAENV if you have superior knowledge. The routine is named ILAENVSET and has the following specification. ILAENVSET (ISPEC,NAME,OPTS,N1,N2,N3,N4,NVALUE,INFO ) [SUBROUTINE] [Input] On input: ISPEC specifies the parameter to be set (see Section 6.2 of the LAPACK User’s Guide for details). INTEGER ISPEC [Input] On input: NAME specifies the name of the LAPACK subroutine for which the parameter is to be set. CHARACTER*(*) NAME CHARACTER*(*) OPTS [Input] On input: OPTS is a character string of options to the subroutine. INTEGER N1, N2, N3, N4 [Input] On input: N1, N2, N3 and N4 are problem dimensions. A value of -1 means that the dimension is unused or irrelevant. [Input] On input: NVALUE is the value to be set for the parameter specified by ISPEC. This value will be retrieved by any future call of ILAENV with similar arguments, including the call of ILAENV coming directly from the routine specified by argument NAME. In most cases, but not all, the value set will apply irrespective of the values of arguments OPTS, N1, N2, N3 and N4. INTEGER NVALUE [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO All arguments of ILAENVSET apart from the last two, NVALUE and INFO, are identical to the arguments of ILAENV. ILAENVSET should be called before you call the LAPACK routine in question. It should be noted that not all LAPACK routines make use of the ILAENV mechanism (because not all routines use blocked algorithms or require other tuning parameters). Calls of ILAENVSET with argument NAME set to the name of such a routine will fail with INFO=0. In addition, the ACML versions of some important routines that do use blocked algorithms, such as the QR factorization routine DGEQRF, bypass ILAENV because they make use of a different tuning system which is independent of standard LAPACK. For all such routines, Chapter 4: LAPACK: Package of Linear Algebra Subroutines 22 ILAENVSET can still be called with no error exit, but calls will have no effect on performance of the routine. Below we give examples of how to call ILAENVSET in both FORTRAN and C. Example (FORTRAN code): C C C INTEGER ILO, IHI, INFO, N, NS CHARACTER COMPZ, JOB INTEGER ILAENV EXTERNAL ILAENV, ILAENVSET JOB = ’E’ COMPZ = ’I’ N = 512 ILO = 1 IHI = 512 Check the default shift parameter (ISPEC=4) used by DHSEQR NS = ILAENV(4, ’DHSEQR’, JOB//COMPZ, N, ILO, IHI, -1) WRITE (*,*) ’Default NS = ’, NS Set a new value 5 for the shift parameter CALL ILAENVSET(4, ’DHSEQR’, JOB//COMPZ, N, ILO, IHI, -1, 5, INFO) Then check the shift parameter again NS = ILAENV(4, ’DHSEQR’, JOB//COMPZ, N, ILO, IHI, -1) WRITE (*,*) ’Revised NS = ’, NS END Example (C code): #include <acml.h> #include <stdio.h> int main(void) { int n=512, ilo=1, ihi=512, ns, info; char compz = ’I’, job = ’E’, opts[3]; opts[0] = job; opts[1] = compz; opts[2] = ’\0’; /* Check the default shift parameter (ISPEC=4) used by DHSEQR */ ns = ilaenv(4, "DHSEQR", opts, n, ilo, ihi, -1); printf("Default ns = %d\n", ns); /* Set a new value 5 for the shift parameter */ ilaenvset(4, "DHSEQR", opts, n, ilo, ihi, -1, 5, &info); /* Then check the shift parameter again */ ns = ilaenv(4, "DHSEQR", opts, n, ilo, ihi, -1); printf("Revised ns = %d\n", ns); return 0; } Chapter 4: LAPACK: Package of Linear Algebra Subroutines 23 4.4 IEEE exceptions and LAPACK Some LAPACK eigensystem routines (namely CHEEVR, DSTEVR, DSYEVR, SSTEVR, SSYEVR, ZHEEVR) are able to take advantage of a faster algorithm when the full eigenspectrum is requested on machines which conform to the IEEE-754 floating point standard [14]. Normal execution of the faster algorithm (implemented by LAPACK routines SSTEGR and DSTEGR, which are called by the routines mentioned above) may create NaNs and infinities and hence may abort due to a floating point exception in environments which do not handle NaNs and infinities in the IEEE standard default manner. This may depend upon the compiler flags used to compile and link the main program. The LAPACK routine ILAENV, called with ISPEC = 10 or 11, states whether or not NaNs or infinities respectively will cause a trap. In ACML, by default ILAENV assumes that NaNs and infinities cause traps, even if this reduces the performance of the eigensystem routines. This is because it is not possible in general to reliably check whether they do trap or not at run-time. The intention is to ensure that these routines always function correctly, irrespective of how the main program calling ACML is compiled. However, if your main program is compiled in such a way that NaNs and infinities do not cause traps, the ACML-specific routine ILAENVSET (see Section 4.3 [ILAENVILAENVSET], page 21) may be used to override the default operative mode of ILAENV, and allow the xxxEVR routines to use the faster xSTEGR algorithm when calculating the full eigenspectrum. When used for this purpose, ILAENVSET should be called as follows: CALL ILAENVSET(10,’X’,’X’,0,0,0,0,1,INFO) CALL ILAENVSET(11,’X’,’X’,0,0,0,0,1,INFO) (or the C equivalent). It is important to note that if you use ILAENVSET in this way before calling an xxxEVR routine, but your program does trap on IEEE exceptions, then there is a chance that your program will terminate unexpectedly. You should consult the documentation for the compiler you are using to find out whether there are compiler flags controlling this. Chapter 5: Fast Fourier Transforms (FFTs) 24 5 Fast Fourier Transforms (FFTs) 5.1 Introduction to FFTs There are two main types of Discrete Fourier Transform (DFT): • routines for the transformation of complex data: in the ACML, these routines have names beginning with ZFFT or CFFT, for double and single precision, respectively; • routines for the transformation of real to complex data and vice versa: in the ACML the names for the former begin with DZFFT or SCFFT, for double and single precision, respectively; the names for the latter begin with ZDFFT or CSFFT. The following subsections provide definitions of the DFT for complex and real data types, and some guidelines on the efficient use of the ACML FFT routines. 5.1.1 Transform definitions and Storage for Complex Data The simplest transforms to describe are those performed on sequences of complex data. Such data are stored as arrays of type complex. The result of a complex FFT is also a complex sequence of the same length and, for the simple interfaces, is written back to the original array. Where multiple (m, say), same-length sequences (of length n) of complex data are to be transformed, the sequences are held in a single complex array; in the simple interfaces the array will be of length m ∗ n containing m end-to-end sequences and the results of the m FFTs are returned in the original array. Expert interfaces are provided which give: greater flexibility in the storage of the original data and results, user provided scaling, and whether results should be written to a separate array or not. The definition of a complex DFT used here is given by: X 1 n−1 2πjk x̃j = √ xk exp ±i n k=0 n for j = 0, 1, . . . , n − 1 where xk are the complex data to be transformed, x̃j are the transformed data, and the sign of ± determines the direction of the transform: (−) for forward and (+) for backward. Note that, in this definition, both directional transforms have the same scaling and performing both consecutively recovers the original data; this is the prescribed scaling provided in the simple FFT interfaces, whereas, in the expert interfaces, the scaling factor must be supplied by the user. For the simple interfaces, a two dimensional array of complex data, with m rows and n columns is stored in the same order as a set of n sequences of length m (as described above). That is, column elements are stored contiguously and the first element of the next column follows the last element of the current column. In the expert interfaces, column elements may be separated by a fixed step length (increment) while row elements may be separated by a second increment; if the first increment is 1 and the second increment is m then we have the same storage as in the simple interface. The definition of a complex 2D DFT used here is given by: x̃jp = √ m−1 X n−1 X 1 2πjk 2πpl xkl exp ±i exp ±i n m m ∗ n l=0 k=0 for j = 0, 1, . . . , n − 1 and l = 0, 1, . . . , m − 1, where xkl are the complex data to be transformed, x̃jp are the transformed data, and the sign of ± determines the direction of the transform. Chapter 5: Fast Fourier Transforms (FFTs) 25 5.1.2 Transform definitions and Storage for Real Data The DFT of a sequence of real data results in a special form of complex sequence known as a Hermitian sequence. The symmetries defining such a sequence mean that it can be fully represented by a set of n real values, where n is the length of the original real sequence. It is therefore conventional for the array containing the real sequence to be overwritten by such a representation of the transformed Hermitian sequence. If the original sequence is purely real valued, i.e. zj = xj , then the definition of the real DFT used here is given by: X 1 n−1 2πjk z̃j = aj + ibj = √ xk exp −i n k=0 n for j = 0, 1, . . . , n − 1 where xk are the real data to be transformed, z̃j are the transformed complex data. In full complex representation, the Hermitian sequence would be a sequence of n complex values Z(i) for i = 0, 1, ..., n − 1, where Z(n − j) is the complex conjugate of Z(j) for j = 1, 2, ..., (n − 1)/2; Z(0) is real valued; and, if n is even, Z(n/2) is real valued. In ACML, the representation of Hermitian sequences used on output from DZFFT routines and on input to ZDFFT routines is as follows: let X be an array of length N and with first index 0, • X(i) contains the real part of Z(i) for i = 0, ..., N/2 • X(N − i) contains the imaginary part of Z(i) for i = 1, ..., (N − 1)/2 Also, given a Hermitian sequence, the discrete transform can be written as: n/2−1 X 1 2πjk 2πjk xj = √ ak cos a0 + 2 − bk sin + an/2 n n n k=1 where an/2 = 0 if n is odd, and z̃k = ak + ibk is the Hermitian sequence to be transformed. Note that, in the above definitions, both transforms have the same (negative) sign in the exponent; performing both consecutively does not recover the original data. To recover original real data, or otherwise to perform an inverse transform on a set of Hermitian data, the Hermitian data must be conjugated prior to performing the transform (i.e. changing the sign of the stored imaginary parts). 5.1.3 Efficiency The efficiency of the FFT is maximized by choosing the sequence length to be a power of 2. Good efficiency can also be achieved when the sequence length has small prime factors, up to a factor 13; however, the time taken for an FFT increases as the size of the prime factor increases. Chapter 5: Fast Fourier Transforms (FFTs) 26 5.1.4 Default and Generated Plans For those FFT routines that can be initialized prior to computing the FFTs, the initialization can be performed in one of two ways. In either case, initialization involves the storing of the factorization of N, and the twiddle factors associated with this factorization, in the communication array COMM. The simpler way to initialize is by setting the argument MODE to zero. This means that a default plan, for the given input dimensions, is used to calculate the FFT. This has the advantage that the initialization phase is very quick and is generally a small fraction of the time required to perform the FFT computation. However, for some problem dimensions the default plan may not be optimal, especially where there is a mixture of prime factors. Under some circumstances, optimality of performance of an FFT computation may be crucial. For example, where a very large number of FFTs are to be performed on problems of a fixed size (e.g. N remains the same), then it is best to initialize by setting the argument MODE to 100. This will time a number of plans (this number can be quite large when N has a significant number of prime factors) and initialize using the plan with the best time. Using this form of initialization can, potentially, lead to significant improvements in the performance of the FFT computation for the given dimensions. Where problem dimensions will not change over a number of runs of a program, the communication array could, for example, be written out to a file during an initialization run, and then read in from the same file on subsequent computation runs. This would be effective for problem dimensions that have a large number of possible plans (factor orderings and groupings) and therefore take a significant amount of time to find the optimal plan. Please consult the individual FFT routine documents to determine whether plan generation is enabled. Chapter 5: Fast Fourier Transforms (FFTs) 27 5.2 FFTs on Complex Sequences 5.2.1 FFT of a single sequence The routines documented here compute the discrete Fourier transform (DFT) of a sequence of complex numbers in either single or double precision arithmetic. The DFT is computed using a highly-efficient FFT algorithm. There are two sets of interfaces available: simple drivers and expert drivers. The simple drivers perform in-place transforms on data held contiguously in memory using a fixed scaling factor; these are simpler to use and are sufficient for many problems. The expert drivers offer greater flexibility by including a number of additional arguments. These allow you to control: the scaling factor applied; whether the result should be output to a separate vector; and, the increments used in storing successive elements of both the input sequence and the result. Chapter 5: Fast Fourier Transforms (FFTs) 28 ZFFT1D Routine Documentation ZFFT1D (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT1D. On input: • MODE=0 : only default initializations (specific to N ) are performed; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1D. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1D. • MODE=−2 : initializations and a forward transform are performed. • MODE=2 : initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations are performed, but first a plan is generated. This plan is chosen based on the fastest FFT computation for a subset of all possible plans. INTEGER MODE INTEGER N [Input] On input: N is the length of the complex sequence X [Input/Output] On input: X contains the complex sequence of length N to be transformed. On output: X contains the transformed sequence. COMPLEX*16 X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX*16 COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL ZFFT1D(0,N,X,COMM,INFO) CALL ZFFT1D(-1,N,X,COMM,INFO) CALL ZFFT1D(-1,N,Y,COMM,INFO) DO 10 I = 1, N X(I) = X(I)*DCONJG(Y(I)) CONTINUE CALL ZFFT1D(1,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 29 CFFT1D Routine Documentation CFFT1D (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT1D. On input: • MODE=0 : only default initializations (specific to N ) are performed; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1D. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1D. • MODE=−2 : (default) initializations and a forward transform are performed. • MODE=2 : (default) initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations are performed, but first a plan is generated. This plan is chosen based on the fastest FFT computation for a subset of all possible plans. INTEGER MODE INTEGER N [Input] On input: N is the length of the complex sequence X [Input/Output] On input: X contains the complex sequence of length N to be transformed. On output: X contains the transformed sequence. COMPLEX X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX COMM(5*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL CFFT1D(0,N,X,COMM,INFO) CALL CFFT1D(-1,N,X,COMM,INFO) CALL CFFT1D(-1,N,Y,COMM,INFO) DO 10 I = 1, N X(I) = X(I)*CONJG(Y(I)) CONTINUE CALL CFFT1D(1,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 30 ZFFT1DX Routine Documentation ZFFT1DX (MODE,SCALE,INPL,N,X,INCX,Y,INCY,COMM, INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT1DX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1DX. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1DX. • MODE=−2 : (default) initializations and a forward transform are performed. • MODE=2 : (default) initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE DOUBLE PRECISION SCALE [Input] On input: SCALE is the scaling factor to apply to the output sequence [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequence; otherwise the output sequence is returned in Y. LOGICAL INPL INTEGER N [Input] On input: N is the number of elements to be transformed [Input/Output] On input: X contains the complex sequence of length N to be transformed, with the ith element stored in X(1+(i-1)*INCX). On output: if INPL is .TRUE. then X contains the transformed sequence in the same locations as on input; otherwise X remains unchanged. COMPLEX*16 X(1+(N-1)*INCX) [Input] On input: INCX is the increment used to store successive elements of a sequence in X. Constraint: INCX > 0. INTEGER INCX [Output] On output: if INPL is .FALSE. then Y contains the transformed sequence, with the ith element stored in Y(1+(i-1)*INCY); otherwise Y is not referenced. COMPLEX*16 Y(1+(N-1)*INCY) Chapter 5: Fast Fourier Transforms (FFTs) 31 [Input] On input: INCY is the increment used to store successive elements of a sequence in Y. If INPL is .TRUE. then INCY is not referenced. Constraint: INCY > 0. INTEGER INCY [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX*16 COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward FFTs are performed unscaled and in-place on contiguous vectors X and Y following initialization. Manipulations on resultant Fourier coefficients are stored in X which is then transformed back. 10 SCALE = 1.0D0 INPL = .TRUE. CALL ZFFT1DX(0,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) CALL ZFFT1DX(-1,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) CALL ZFFT1DX(-1,SCALE,INPL,N,Y,1,DUM,1,COMM,INFO) DO 10 I = 1, N X(I) = X(I)*DCONJG(Y(I))/DBLE(N) CONTINUE CALL ZFFT1DX(1,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 32 CFFT1DX Routine Documentation CFFT1DX (MODE,SCALE,INPL,N,X,INCX,Y,INCY,COMM, INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT1DX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1DX. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1DX. • MODE=−2 : (default) initializations and a forward transform are performed. • MODE=2 : (default) initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE REAL SCALE [Input] On input: SCALE is the scaling factor to apply to the output sequence [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequence; otherwise the output sequence is returned in Y. LOGICAL INPL INTEGER N [Input] On input: N is the number of elements to be transformed [Input/Output] On input: X contains the complex sequence of length N to be transformed, with the ith element stored in X(1+(i-1)*INCX). On output: if INPL is .TRUE. then X contains the transformed sequence in the same locations as on input; otherwise X remains unchanged. COMPLEX X(1+(N-1)*INCX) [Input] On input: INCX is the increment used to store successive elements of a sequence in X. Constraint: INCX > 0. INTEGER INCX [Output] On output: if INPL is .FALSE. then Y contains the transformed sequence, with the ith element stored in Y(1+(i-1)*INCY); otherwise Y is not referenced. COMPLEX Y(1+(N-1)*INCY) Chapter 5: Fast Fourier Transforms (FFTs) 33 [Input] On input: INCY is the increment used to store successive elements of a sequence in Y. If INPL is .TRUE. then INCY is not referenced. Constraint: INCY > 0. INTEGER INCY [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX COMM(5*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward FFTs are performed unscaled and in-place on contiguous vectors X and Y following initialization. Manipulations on resultant Fourier coefficients are stored in X which is then transformed back. 10 SCALE = 1.0 INPL = .TRUE. CALL CFFT1DX(0,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) CALL CFFT1DX(-1,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) CALL CFFT1DX(-1,SCALE,INPL,N,Y,1,DUM,1,COMM,INFO) DO 10 I = 1, N X(I) = X(I)*CONJG(Y(I))/REAL(N) CONTINUE CALL CFFT1DX(1,SCALE,INPL,N,X,1,DUM,1,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 34 5.2.2 FFT of multiple complex sequences The routines documented here compute the discrete Fourier transforms (DFTs) of a number of sequences of complex numbers in either single or double precision arithmetic. The sequences must all have the same length. The DFTs are computed using a highly-efficient FFT algorithm. There are two sets of interfaces available: simple drivers and expert drivers. The simple drivers perform in-place transforms on data held contiguously in memory using a fixed scaling factor; these are simpler to use and are sufficient for many problems. The expert drivers offer greater flexibility by including a number of additional arguments. These allow you to control: the scaling factor applied; whether the result should be output to a separate vector; the increments used in storing successive elements of a given sequence (for both input and output sequences); and the increments used in storing corresponding elements in successive sequences (for both input and output). Chapter 5: Fast Fourier Transforms (FFTs) 35 ZFFT1M Routine Documentation ZFFT1M (MODE,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT1M. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : forward transforms are performed. Initializations are assumed to have been performed by a prior call to ZFFT1M. • MODE=1 : backward (reverse) transforms are performed. Initializations are assumed to have been performed by a prior call to ZFFT1M. • MODE=−2 : (default) initializations and forward transforms are performed. • MODE=2 : (default) initializations and backward transforms are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE INTEGER M [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the complex sequences in X [Input/Output] On input: X contains the M complex sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. On output: X contains the transformed sequences. COMPLEX*16 X(N*M) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX*16 COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 5: Fast Fourier Transforms (FFTs) Example: 10 CALL ZFFT1M(0,1,N,X,COMM,INFO) CALL ZFFT1M(-1,2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*DCONJG(X(I,2)) X(I,2) = DCMPLX(0.0D0,1.0D0)*X(I,2) CONTINUE CALL ZFFT1M(1,2,N,X(1,2),COMM,INFO) 36 Chapter 5: Fast Fourier Transforms (FFTs) 37 CFFT1M Routine Documentation CFFT1M (MODE,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT1M. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : forward transforms are performed. Initializations are assumed to have been performed by a prior call to CFFT1M. • MODE=1 : backward (reverse) transforms are performed. Initializations are assumed to have been performed by a prior call to CFFT1M. • MODE=−2 : (default) initializations and forward transforms are performed. • MODE=2 : (default) initializations and backward transforms are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE INTEGER M [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the complex sequences in X [Input/Output] On input: X contains the M complex sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. On output: X contains the transformed sequences. COMPLEX X(N*M) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX COMM(5*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 5: Fast Fourier Transforms (FFTs) Example: 10 CALL CFFT1M(0,1,N,X,COMM,INFO) CALL CFFT1M(-1,2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*CONJG(X(I,2)) X(I,2) = CMPLX(0.0D0,1.0D0)*X(I,2) CONTINUE CALL CFFT1M(1,2,N,X(1,2),COMM,INFO) 38 Chapter 5: Fast Fourier Transforms (FFTs) 39 ZFFT1MX Routine Documentation ZFFT1MX (MODE,SCALE,INPL,NSEQ,N,X,INCX1,INCX2, Y,INCY1,INCY2,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT1MX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1MX. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT1MX. • MODE=−2 : (default) initializations and a forward transform are performed. • MODE=2 : (default) initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences DOUBLE PRECISION SCALE [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER NSEQ [Input] On input: NSEQ is the number of sequences to be transformed [Input] On input: N is the number of elements in each sequence to be transformed INTEGER N [Input/Output] On input: X contains the NSEQ complex sequences of length N to be transformed; the ith element of sequence j is stored in X(1+(i-1)*INCX1+(j1)*INCX2). On output: if INPL is .TRUE. then X contains the transformed sequences in the same locations as on input; otherwise X remains unchanged. COMPLEX*16 X(1+(N-1)*INCX1+(NSEQ-1)*INCX2) [Input] On input: INCX1 is the increment used to store successive elements of a given sequence in X (INCX1=1 for contiguous data). Constraint: INCX1 > 0. INTEGER INCX1 Chapter 5: Fast Fourier Transforms (FFTs) 40 [Input] On input: INCX2 is the increment used to store corresponding elements of successive sequences in X (INCX2=N for contiguous data). Constraint: INCX2 > 0. INTEGER INCX2 [Output] On output: if INPL is .FALSE. then Y contains the transformed sequences with the ith element of sequence j stored in Y(1+(i-1)*INCY1+(j-1)*INCY2); otherwise Y is not referenced. COMPLEX*16 Y(1+(N-1)*INCY1+(NSEQ-1)*INCY2) [Input] On input: INCY1 is the increment used to store successive elements of a given sequence in Y. If INPL is .TRUE. then INCY1 is not referenced. Constraint: INCY1 > 0. INTEGER INCY1 [Input] On input: INCY2 is the increment used to store corresponding elements of successive sequences in Y (INCY2=N for contiguous data). If INPL is .TRUE. then INCY2 is not referenced. Constraint: INCY2 > 0. INTEGER INCY2 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX*16 COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward FFTs are performed unscaled and in-place on two contiguous vectors stored in the first two columns of X. Manipulations are stored in 2nd and 3rd columns of X which are then transformed back. 10 COMPLEX *16 X(N,3) SCALE = 1.0D0 INPL = .TRUE. CALL ZFFT1MX(0,SCALE,INPL,2,N,X,1,N,DUM,1,N,COMM,INFO) CALL ZFFT1MX(-1,SCALE,INPL,2,N,X,1,N,DUM,1,N,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*DCONJG(X(I,2))/DBLE(N) X(I,2) = DCMPLX(0.0D0,1.0D0)*X(I,2)/DBLE(N) CONTINUE CALL ZFFT1MX(1,SCALE,INPL,2,N,X(1,2),1,N,DUM,1,N,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 41 CFFT1MX Routine Documentation CFFT1MX (MODE,SCALE,INPL,NSEQ,N,X,INCX1,INCX2, Y,INCY1,INCY2,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT1MX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1MX. • MODE=1 : a backward (reverse) transform is performed. Initializations are assumed to have been performed by a prior call to CFFT1MX. • MODE=−2 : (default) initializations and a forward transform are performed. • MODE=2 : (default) initializations and a backward transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences REAL SCALE [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER NSEQ [Input] On input: NSEQ is the number of sequences to be transformed [Input] On input: N is the number of elements in each sequence to be transformed INTEGER N [Input/Output] On input: X contains the NSEQ complex sequences of length N to be transformed; the ith element of sequence j is stored in X(1+(i-1)*INCX1+(j1)*INCX2). On output: if INPL is .TRUE. then X contains the transformed sequences in the same locations as on input; otherwise X remains unchanged. COMPLEX X(1+(N-1)*INCX1+(NSEQ-1)*INCX2) [Input] On input: INCX1 is the increment used to store successive elements of a given sequence in X (INCX1=1 for contiguous data). Constraint: INCX1 > 0. INTEGER INCX1 Chapter 5: Fast Fourier Transforms (FFTs) 42 [Input] On input: INCX2 is the increment used to store corresponding elements of successive sequences in X (INCX2=N for contiguous data). Constraint: INCX2 > 0. INTEGER INCX2 [Output] On output: if INPL is .FALSE. then Y contains the transformed sequences with the ith element of sequence j stored in Y(1+(i-1)*INCY1+(j-1)*INCY2); otherwise Y is not referenced. COMPLEX Y(1+(N-1)*INCY1+(NSEQ-1)*INCY2) [Input] On input: INCY1 is the increment used to store successive elements of a given sequence in Y. If INPL is .TRUE. then INCY1 is not referenced. Constraint: INCY1 > 0. INTEGER INCY1 [Input] On input: INCY2 is the increment used to store corresponding elements of successive sequences in Y (INCY2=N for contiguous data). If INPL is .TRUE. then INCY2 is not referenced. Constraint: INCY2 > 0. INTEGER INCY2 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. COMPLEX COMM(5*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward FFTs are performed unscaled and in-place on two contiguous vectors stored in the first two columns of X. Manipulations are stored in 2nd and 3rd columns of X which are then transformed back. 10 COMPLEX X(N,3) SCALE = 1.0 INPL = .TRUE. CALL CFFT1MX(0,SCALE,INPL,2,N,X,1,N,DUM,1,N,COMM,INFO) CALL CFFT1MX(-1,SCALE,INPL,2,N,X,1,N,DUM,1,N,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*CONJG(X(I,2))/REAL(N) X(I,2) = CMPLX(0.0D0,1.0D0)*X(I,2)/REAL(N) CONTINUE CALL CFFT1MX(1,SCALE,INPL,2,N,X(1,2),1,N,DUM,1,N,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 43 5.2.3 2D FFT of two-dimensional arrays of data The routines documented here compute the two-dimensional discrete Fourier transforms (DFT) of a two-dimensional array of complex numbers in either single or double precision arithmetic. The 2D DFT is computed using a highly-efficient FFT algorithm. There are two sets of interfaces available: simple drivers and expert drivers. The simple drivers perform in-place transforms on data held contiguously in memory using a fixed scaling factor; these are simpler to use and are sufficient for many problems. The expert drivers offer greater flexibility by including a number of additional arguments. These allow you to control: the scaling factor applied; whether the result should be output to a separate array; the increments used in storing successive elements in each dimension (for both input and output); and the facility to not perform a final transposition. This final facility is useful for those cases where a forward and backward transform are to be applied with some data manipulations in between; here two whole transpositions can be saved. Chapter 5: Fast Fourier Transforms (FFTs) 44 ZFFT2D Routine Documentation ZFFT2D (MODE,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the direction of transform to be performed by ZFFT2D. On input: • MODE=−1 : forward 2D transform is performed. • MODE=1 : backward (reverse) 2D transform is performed. INTEGER MODE [Input] On input: M is the number of rows in the 2D array of data to be transformed. If X is declared as a 2D array then M is the first dimension of X. INTEGER M [Input] On input: N is the number of columns in the 2D array of data to be transformed. If X is declared as a 2D array then M is the second dimension of X. INTEGER N [Input/Output] On input: X contains the M by N complex 2D array to be transformed. Element ij is stored in location i + (j − 1) ∗ M of X. On output: X contains the transformed sequence. COMPLEX*16 X(M*N) COMPLEX*16 COMM(M*N+3*(M+N)) [Input/Output] COMM is a communication array used as temporary store. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 20 CALL ZFFT2D(-1,M,N,X,COMM,INFO) DO 20 J = 1, N DO 10 I = 1, MIN(J-1,M) X(I,J) = 0.5D0*(X(I,J) + X(J,I)) X(J,I) = DCONJG(X(I,J)) CONTINUE CONTINUE CALL ZFFT2D(1,M,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 45 CFFT2D Routine Documentation CFFT2D (MODE,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the direction of transform to be performed by CFFT2D. On input: • MODE=−1 : a forward 2D transform is performed. • MODE=1 : a backward (reverse) 2D transform is performed. INTEGER MODE [Input] On input: M is the number of rows in the 2D array of data to be transformed. If X is declared as a 2D array then M is the first dimension of X. INTEGER M [Input] On input: N is the number of columns in the 2D array of data to be transformed. If X is declared as a 2D array then M is the second dimension of X. INTEGER N [Input/Output] On input: X contains the M by N complex 2D array to be transformed. Element ij is stored in location i + (j − 1) ∗ M of X. On output: X contains the transformed sequence. COMPLEX X(M*N) COMPLEX COMM(M*N+5*(M+N)) [Input/Output] COMM is a communication array used as temporary store. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 20 CALL CFFT2D(-1,M,N,X,COMM,INFO) DO 20 J = 1, N DO 10 I = 1, MIN(J-1,M) X(I,J) = 0.5D0*(X(I,J) + X(J,I)) X(J,I) = CONJG(X(I,J)) CONTINUE CONTINUE CALL CFFT2D(1,M,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 46 ZFFT2DX Routine Documentation ZFFT2DX (MODE,SCALE,LTRANS,INPL,M,N,X,INCX1, INCX2,Y,INCY1,INCY2,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT2DX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 2D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT2DX. • MODE=1 : a backward (reverse) 2D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT2DX. • MODE=−2 : (default) initializations and a forward 2D transform are performed. • MODE=2 : (default) initializations and a backward 2D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of N and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of N and M. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences DOUBLE PRECISION SCALE [Input] On input: if LTRANS is .TRUE. then a normal final transposition is performed internally to return transformed data consistent with the values for arguments INPL, INCX1, INCX2, INCY1 and INCY2. If LTRANS is .FALSE. then the final transposition is not performed explicitly; the storage format on output is determined by whether the output data is stored contiguously or not – please see the output specifications for X and Y for details. LOGICAL LTRANS [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER M [Input] On input: M is the first dimension of the 2D transform. INTEGER N On input: N is the second dimension of the 2D transform. [Input] Chapter 5: Fast Fourier Transforms (FFTs) 47 [Input/Output] On input: X contains the M by N complex 2D data array to be transformed; the (ij)th element is stored in X(1+(i-1)*INCX1+(j-1)*INCX2). On output: if INPL is .TRUE. then X contains the transformed data, either in the same locations as on input when LTRANS=.TRUE.; in locations X((i1)*N+j) when LTRANS=.FALSE., INCX1=1 and INCX2=M; and otherwise in the same locations as on input. If INPL is .FALSE. X remains unchanged. COMPLEX*16 X(1+(M-1)*INCX1+(N-1)*INCX2) [Input] On input: INCX1 is the increment used to store, in X, successive elements in the first dimension (INCX1=1 for contiguous data). Constraint: INCX1 > 0. INTEGER INCX1 [Input] On input: INCX2 is the increment used to store, in X, successive elements in the second dimension (INCX2=M for contiguous data). Constraint: INCX2 > 0; INCX2 > (M-1)*INCX1 if N > 1. INTEGER INCX2 [Output] On output: if INPL is .FALSE. then Y contains the transformed data. If LTRANS=.TRUE. then the (ij)th data element is stored in Y(1+(i1)*INCY1+(j-1)*INCY2); if LTRANS=.FALSE., INCY1=1 and INCY2=N then the (ij)th data element is stored in Y((i-1)*N+j); and otherwise the (ij)th element is stored in Y(1+(i-1)*INCY1+(j-1)*INCY2). If INPL is .TRUE. then Y is not referenced. COMPLEX*16 Y(1+(M-1)*INCY1+(N-1)*INCY2) [Input] On input: INCY1 is the increment used to store successive elements in the first dimension in Y (INCY1=1 for contiguous data). If INPL is .TRUE. then INCY1 is not referenced. Constraint: INCY1 > 0. INTEGER INCY1 [Input] On input: INCY2 is the increment used to store successive elements in the second dimension in Y (for contiguous data, INCY2=M when LTRANS is .TRUE. or INCY2=N when LTRANS is .FALSE.). If INPL is .TRUE. then INCY2 is not referenced. Constraints: INCY2 > 0; INCY2 > (M-1)*INCY1 if N > 1 and LTRANS is .TRUE.; INCY2 = N if M > 1 and LTRANS is .FALSE.. INTEGER INCY2 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same dimensions M and N. The remainder is used as temporary store. COMPLEX*16 COMM(M*N+3*M+3*N+200) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 5: Fast Fourier Transforms (FFTs) Example: C C C C C Forward 2D FFT is performed unscaled, without final transpose and out-of-place on data stored in array X and output to Y. Manipulations are stored in vector Y which is then transformed back, with scaling, into the first M rows of X. 10 20 48 COMPLEX *16 X(M,N), Y(N,M) SCALE = 1.0D0 INPL = .FALSE. LTRANS = .FALSE. CALL ZFFT2DX(0,SCALE,LTRANS,INPL,M,N,X,1,M,Y,1,N,COMM,INFO) CALL ZFFT2DX(-1,SCALE,LTRANS,INPL,M,N,X,1,M,Y,1,N,COMM,INFO) DO 20 I = M DO 10 J = 1, N Y(J,I) = 0.5D0*Y(J,I)*EXP(0.001D0*(I+J-2)) CONTINUE CONTINUE SCALE = 1.0D0/DBLE(M*N) CALL ZFFT2DX(1,SCALE,LTRANS,INPL,N,M,Y,1,N,X,1,M,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 49 CFFT2DX Routine Documentation CFFT2DX (MODE,SCALE,LTRANS,INPL,M,N,X,INCX1, INCX2,Y,INCY1,INCY2,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT2DX. On input: • MODE=0 : only initializations (specific to the value of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 2D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT2DX. • MODE=1 : a backward (reverse) 2D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT2DX. • MODE=−2 : (default) initializations and a forward 2D transform are performed. • MODE=2 : (default) initializations and a backward 2D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of N and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of N and M. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences REAL SCALE [Input] On input: if LTRANS is .TRUE. then a normal final transposition is performed internally to return transformed data consistent with the values for arguments INPL, INCX1, INCX2, INCY1 and INCY2. If LTRANS is .FALSE. then the final transposition is not performed explicitly; the storage format on output is determined by whether the output data is stored contiguously or not – please see the output specifications for X and Y for details. LOGICAL LTRANS [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER M [Input] On input: M is the first dimension of the 2D transform. INTEGER N On input: N is the second dimension of the 2D transform. [Input] Chapter 5: Fast Fourier Transforms (FFTs) 50 [Input/Output] On input: X contains the M by N complex 2D data array to be transformed; the (ij)th element is stored in X(1+(i-1)*INCX1+(j-1)*INCX2). On output: if INPL is .TRUE. then X contains the transformed data, either in the same locations as on input when LTRANS=.TRUE.; in locations X((i1)*N+j) when LTRANS=.FALSE., INCX1=1 and INCX2=M; and otherwise in the same locations as on input. If INPL is .FALSE. X remains unchanged. COMPLEX X(1+(M-1)*INCX1+(N-1)*INCX2) [Input] On input: INCX1 is the increment used to store, in X, successive elements in the first dimension (INCX1=1 for contiguous data). Constraint: INCX1 > 0. INTEGER INCX1 [Input] On input: INCX2 is the increment used to store, in X, successive elements in the second dimension (INCX2=M for contiguous data). Constraint: INCX2 > 0; INCX2 > (M-1)*INCX1 if N > 1. INTEGER INCX2 [Output] On output: if INPL is .FALSE. then Y contains the transformed data. If LTRANS=.TRUE. then the (ij)th data element is stored in Y(1+(i1)*INCY1+(j-1)*INCY2); if LTRANS=.FALSE., INCY1=1 and INCY2=N then the (ij)th data element is stored in Y((i-1)*N+j); and otherwise the (ij)th element is stored in Y(1+(i-1)*INCY1+(j-1)*INCY2). If INPL is .TRUE. then Y is not referenced. COMPLEX Y(1+(M-1)*INCY1+(N-1)*INCY2) [Input] On input: INCY1 is the increment used to store successive elements in the first dimension in Y (INCY1=1 for contiguous data). If INPL is .TRUE. then INCY1 is not referenced. Constraint: INCY1 > 0. INTEGER INCY1 [Input] On input: INCY2 is the increment used to store successive elements in the second dimension in Y (for contiguous data, INCY2=M when LTRANS is .TRUE. or INCY2=N when LTRANS is .FALSE.). If INPL is .TRUE. then INCY2 is not referenced. Constraints: INCY2 > 0; INCY2 > (M-1)*INCY1 if N > 1 and LTRANS is .TRUE.; INCY2 = N if M > 1 and LTRANS is .FALSE.. INTEGER INCY2 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same dimensions M and N. The remainder is used as temporary store. COMPLEX COMM(M*N+5*M+5*N+200) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 5: Fast Fourier Transforms (FFTs) Example: C C C C C Forward 2D FFT is performed unscaled, without final transpose and out-of-place on data stored in array X and output to Y. Manipulations are stored in vector Y which is then transformed back, with scaling, into the first M rows of X. 10 20 51 COMPLEX X(M,N), Y(N,M) SCALE = 1.0 INPL = .FALSE. LTRANS = .FALSE. CALL CFFT2DX(0,SCALE,LTRANS,INPL,M,N,X,1,M,Y,1,N,COMM,INFO) CALL CFFT2DX(-1,SCALE,LTRANS,INPL,M,N,X,1,M,Y,1,N,COMM,INFO) DO 20 I = M DO 10 J = 1, N Y(J,I) = 0.5*Y(J,I)*EXP(-0.001*REAL(I+J-2)) IY = IY + 1 CONTINUE CONTINUE SCALE = 1.0/REAL(M*N) CALL CFFT2DX(1,SCALE,LTRANS,INPL,N,M,Y,1,N,X,1,M,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 52 5.2.4 3D FFT of three-dimensional arrays of data The routines documented here compute the three-dimensional discrete Fourier transforms (DFT) of a three-dimensional array of complex numbers in either single or double precision arithmetic. The 3D DFT is computed using a highly-efficient FFT algorithm. Please note that at Release 2.7 of ACML it was necessary to modify slightly the interfaces of two of the expert FFT drivers introduced at Release 2.2 of ACML. The two routines are CFFT3DX and ZFFT3DX. The changes were required to permit the optimization of these routines by adding an initialization stage which can then use the plan generator (MODE=100) to select the optimal plan. User codes that called CFFT3DX or ZFFT3DX using a release of ACML prior to 2.7 will need to be modified in one of two ways. Calls to CFFT3DX/ZFFT3DX with MODE = -1 or 1 can be fixed for ACML Release 2.7 and later by either: • preceding the call with a call setting MODE = 0 (default initialization), or MODE = 100 (initialization using plan generator); or, • doubling the MODE argument value to MODE = -2 or 2 respectively (thus incorporating default initialization). Additionally, the minimum length of the communication (work)space arrays in CFFT3DX and ZFFT3DX has been increased by 100 to allow for plan storage. Please consult the individual routine documents for full details on their use. Chapter 5: Fast Fourier Transforms (FFTs) 53 ZFFT3D Routine Documentation ZFFT3D (MODE,L,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the direction of transform to be performed by ZFFT3D. On input: • MODE=−1 : forward 3D transform is performed. • MODE=1 : backward (reverse) 3D transform is performed. INTEGER MODE [Input] On input: the length of the first dimension of the 3D array of data to be transformed. If X is declared as a 3D array then L is the first dimension of X. INTEGER L [Input] On input: the length of the second dimension of the 3D array of data to be transformed. If X is declared as a 3D array then M is the second dimension of X. INTEGER M [Input] On input: the length of the third dimension of the 3D array of data to be transformed. If X is declared as a 3D array then N is the third dimension of X. INTEGER N [Input/Output] On input: X contains the L by M by N complex 3D array to be transformed. Element ijk is stored in location i + (j − 1) ∗ L + (k − 1) ∗ L ∗ M of X. On output: X contains the transformed sequence. COMPLEX*16 X(L*M*N) COMPLEX*16 COMM(L*M*N+3*(L+M+N)) [Input/Output] COMM is a communication array used as temporary store. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 20 30 CALL ZFFT3D(-1,L,M,N,X,COMM,INFO) DO 30 K = 1, N DO 20 J = 1, M DO 10 I = 1, L X(I,J) = X(I,J)*EXP(-0.001D0*DBLE(I+J+K)) CONTINUE CONTINUE CONTINUE CALL ZFFT3D(1,L,M,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 54 CFFT3D Routine Documentation CFFT3D (MODE,L,M,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the direction of transform to be performed by CFFT3D. On input: • MODE=−1 : forward 3D transform is performed. • MODE=1 : backward (reverse) 3D transform is performed. INTEGER MODE [Input] On input: the length of the first dimension of the 3D array of data to be transformed. If X is declared as a 3D array then L is the first dimension of X. INTEGER L [Input] On input: the length of the second dimension of the 3D array of data to be transformed. If X is declared as a 3D array then M is the second dimension of X. INTEGER M [Input] On input: the length of the third dimension of the 3D array of data to be transformed. If X is declared as a 3D array then N is the third dimension of X. INTEGER N [Input/Output] On input: X contains the L by M by N complex 3D array to be transformed. Element ijk is stored in location i + (j − 1) ∗ L + (k − 1) ∗ L ∗ M of X. On output: X contains the transformed sequence. COMPLEX X(L*M*N) [Input/Output] COMM is a communication array used as temporary store. Note that the amount of store explicitly required here is less than in some versions prior to this release (version 4.1 and older). Some further workspace will be allocated internally; the amount of allocated memory requested will be in decreasing size until the allocation is successful, and the initial request will be approximately proportional to the square of max(L,M,N ). The algorithm chosen will change with each request; the algorithm associated with the first request will generally be the fastest. COMPLEX COMM(5*(L+M+N)+4) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 5: Fast Fourier Transforms (FFTs) Example: 10 20 30 CALL CFFT3D(-1,L,M,N,X,COMM,INFO) DO 30 K = 1, N DO 20 J = 1, M DO 10 I = 1, L X(I,J) = X(I,J)*EXP(-0.001D0*REAL(I+J+K)) CONTINUE CONTINUE CONTINUE CALL CFFT3D(1,L,M,N,X,COMM,INFO) 55 Chapter 5: Fast Fourier Transforms (FFTs) 56 ZFFT3DX Routine Documentation ZFFT3DX (MODE,SCALE,LTRANS,INPL,L,M,N,X,Y, COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZFFT3DX. On input: • MODE=0 : only initializations (specific to the values of L, M and N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 3D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT3DX. • MODE=1 : a backward (reverse) 3D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT3DX. • MODE=−2 : (default) initializations and a forward 3D transform are performed. • MODE=2 : (default) initializations and a backward 3D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of L, M and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of L, M and N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences DOUBLE PRECISION SCALE [Input] On input: if LTRANS is .TRUE. then a normal final transposition is performed internally to return transformed data using the same storage format as the input data. If LTRANS is .FALSE. then the final transposition is not performed and transformed data is stored, in X or Y, in transposed form. LOGICAL LTRANS [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER L [Input] On input: L is the first dimension of the 3D transform. INTEGER M [Input] On input: M is the second dimension of the 3D transform. INTEGER N On input: N is the third dimension of the 3D transform. [Input] Chapter 5: Fast Fourier Transforms (FFTs) 57 [Input/Output] On input: X contains the L by M by N complex 3D data array to be transformed; the (ijk)th element is stored in X(i+(j-1)*L+(k-1)*L*M). On output: if INPL is .TRUE. then X contains the transformed data, either in the same locations as on input when LTRANS=.TRUE.; or in locations X(k+(j-1)*N+(i-1)*N*M) when LTRANS=.FALSE. If INPL is .FALSE. X remains unchanged. COMPLEX*16 X(L*M*N) [Output] On output: if INPL is .FALSE. then Y contains the three-dimensional transformed data. If LTRANS=.TRUE. then the (ijk)th data element is stored in Y(i+(j-1)*L+(k-1)*L*M); otherwise, the (ijk)th data element is stored in Y(k+(j1)*N+(i-1)*N*M). If INPL is .TRUE. then Y is not referenced. COMPLEX*16 Y(L*M*N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence dimensions. The remainder is used as temporary store. COMPLEX*16 COMM(L*M*N+3*(L+M+N)+300) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward 3D FFT is performed unscaled, without final transpose and out-of-place on data stored in array X and output to Y. Manipulations are stored in vector Y which is then transformed back, with scaling, into the first M rows of X. 10 20 COMPLEX *16 X(L*M*N), Y(L*M*N) SCALE = 1.0D0 INPL = .FALSE. LTRANS = .FALSE. CALL ZFFT3DX(0,SCALE,LTRANS,INPL,L,M,N,X,Y,COMM,INFO) CALL ZFFT3DX(-1,SCALE,LTRANS,INPL,L,M,N,X,Y,COMM,INFO) IY = 1 DO 20 I = 1, L DO 40 J = 1, M DO 10 K = 1, N Y(IY) = Y(IY)*EXP(-0.001D0*DBLE(I+J+K-3)) IY = IY + 1 CONTINUE CONTINUE SCALE = 1.0D0/DBLE(L*M*N) CALL ZFFT3DX(1,SCALE,LTRANS,INPL,N,M,L,Y,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 58 CFFT3DX Routine Documentation CFFT3DX (MODE,SCALE,LTRANS,INPL,L,M,N,X,Y, COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CFFT3DX. On input: • MODE=0 : only initializations (specific to the values of L, M and N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 3D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT3DX. • MODE=1 : a backward (reverse) 3D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT3DX. • MODE=−2 : (default) initializations and a forward 3D transform are performed. • MODE=2 : (default) initializations and a backward 3D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of L, M and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of L, M and N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences REAL SCALE [Input] On input: if LTRANS is .TRUE. then a normal final transposition is performed internally to return transformed data using the same storage format as the input data. If LTRANS is .FALSE. then the final transposition is not performed and transformed data is stored, in X or Y, in transposed form. LOGICAL LTRANS [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER L [Input] On input: L is the first dimension of the 3D transform. INTEGER M [Input] On input: M is the second dimension of the 3D transform. INTEGER N On input: N is the third dimension of the 3D transform. [Input] Chapter 5: Fast Fourier Transforms (FFTs) 59 [Input/Output] On input: X contains the L by M by N complex 3D data array to be transformed; the (ijk)th element is stored in X(i+(j-1)*L+(k-1)*L*M). On output: if INPL is .TRUE. then X contains the transformed data, either in the same locations as on input when LTRANS=.TRUE.; or in locations X(k+(j-1)*N+(i-1)*N*M) when LTRANS=.FALSE. If INPL is .FALSE. X remains unchanged. COMPLEX X(L*M*N) [Output] On output: if INPL is .FALSE. then Y contains the three-dimensional transformed data. If LTRANS=.TRUE. then the (ijk)th data element is stored in Y(i+(j-1)*L+(k-1)*L*M); otherwise, the (ijk)th data element is stored in Y(k+(j1)*N+(k-1)*N*M). If INPL is .TRUE. then Y is not referenced. COMPLEX Y(L*M*N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence dimensions. The remainder is used as temporary store. COMPLEX COMM(L*M*N+5*(L+M+N)+300) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C C Forward 3D FFT is performed unscaled, without final transpose and out-of-place on data stored in array X and output to Y. Manipulations are stored in vector Y which is then transformed back, with scaling, into the first M rows of X. 10 20 SCALE = 1.0 INPL = .FALSE. LTRANS = .FALSE. CALL CFFT3DX(0,SCALE,LTRANS,INPL,L,M,N,X,Y,COMM,INFO) CALL CFFT3DX(-1,SCALE,LTRANS,INPL,L,M,N,X,Y,COMM,INFO) IY = 1 DO 20 I = 1, L DO 40 J = 1, M DO 10 K = 1, N Y(IY) = Y(IY)*EXP(-0.001*REAL(I+J+K-3)) IY = IY + 1 CONTINUE CONTINUE SCALE = 1.0/REAL(L*M*N) CALL CFFT3DX(1,SCALE,LTRANS,INPL,N,M,L,Y,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 60 ZFFT3DY Routine Documentation ZFFT3DY (MODE,SCALE,INPL,L,M,N,X, [SUBROUTINE] INCX1,INCX2,INCX3,Y,INCY1,INCY2,INCY3,COMM,LCOMM,INFO ) [Input] The value of MODE on input determines the operation performed by ZFFT3DY. On input: • MODE=0 : only initializations (specific to the values of L, M and N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 3D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT3DY. • MODE=1 : a backward (reverse) 3D transform is performed. Initializations are assumed to have been performed by a prior call to ZFFT3DY. • MODE=−2 : (default) initializations and a forward 3D transform are performed. • MODE=2 : (default) initializations and a backward 3D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of L, M and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of L, M and N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences REAL SCALE [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER L [Input] On input: L is the first dimension of the 3D transform. INTEGER M [Input] On input: M is the second dimension of the 3D transform. INTEGER N [Input] On input: N is the third dimension of the 3D transform. [Input/Output] On input: X contains the L by M by N complex 3D data array to be transformed; the (ijk)th element is stored in X(1+(i-1)*INCX1+(j-1)*INCX2+(k1)*INCX3). On output: if INPL is .TRUE. then X contains the transformed data in the same locations as on input. If INPL is .FALSE. X remains unchanged. COMPLEX*16 X(*) Chapter 5: Fast Fourier Transforms (FFTs) 61 [Input] On input: INCX1 is the step in index of X between successive data elements in the first dimension of the 3D data. Usually INCX1=1 so that succesive elements in the first dimension are stored contiguously. Constraint: INCX1 > 0. INTEGER INCX1 [Input] On input: INCX2 is the step in index of X between successive data elements in the second dimension of the 3D data. For completely contiguous data (no gaps in X ) INCX2 should be set to L. Constraint: INCX2 > 0; INCX2 > (L-1)*INCX1 if max(M,N) > 1. INTEGER INCX2 [Input] On input: INCX3 is the step in index of X between successive data elements in the third dimension of the 3D data. For completely contiguous data (no gaps in X ) INCX3 should be set to L*M. Constraint: INCX3 > 0; INCX3 > (L-1)*INCX1+(M-1)*INCX2 if N > 1. INTEGER INCX3 [Output] On output: if INPL is .FALSE. then Y contains the three-dimensional transformed data. If LTRANS=.TRUE. then the the (ijk)th element is stored in Y(1+(i-1)*INCY1+(j-1)*INCY2+(k-1)*INCY3). If INPL is .TRUE. then Y is not referenced. COMPLEX*16 Y(*) [Input] On input: if INPL is .FALSE. then INCY1 is the step in index of Y between successive data elements in the first dimension of the 3D transformed data. Usually INCY1=1 so that succesive elements in the first dimension are stored contiguously. If INPL is .TRUE. then INCY1 is not referenced. Constraint: If INPL is .FALSE. then INCY1 > 0. INTEGER INCY1 [Input] On input: if INPL is .FALSE. then INCY2 is the step in index of Y between successive data elements in the second dimension of the 3D transformed data. For completely contiguous data (no gaps in Y ) INCY2 should be set to L. Constraint: INCY2 > 0 if INPL is .FALSE.; INCY2 > (L-1)*INCY1, if INPL is .FALSE. and max(M,N) > 1. INTEGER INCY2 [Input] On input: if INPL is .FALSE. then INCY3 is the step in index of Y between successive data elements in the third dimension of the 3D transformed data. For completely contiguous data (no gaps in Y ) INCY3 should be set to L*M. Constraint: INCY3 > 0 if INPL is .FALSE.; INCY3 > (L-1)*INCY1+(M-1)*INCY2, if INPL is .FALSE. and N > 1. INTEGER INCY3 Chapter 5: Fast Fourier Transforms (FFTs) 62 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence dimensions. The remainder is used as temporary store; if this is not sufficient for the requirements of the routine then temporary storage space will be dynamically allocated internally. COMPLEX*16 COMM(LCOMM ) [Input] On input: LCOMM is the length of the communication array COMM. The amount of internal dynamic allocation of temporary storage can be reduced significantly by declaring COMM to be of length at least L*M*N + 4*(L+M+N) + 300. Constraint: LCOMM > 3*(L+M+N) + 150. INTEGER LCOMM [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C Forward 3D FFT is performed unscaled and in-place, on the leading 10x10x10 submatrix of a larger 100x100x100 array of data. The result is transformed back with scaling. * * 10 20 * SCALE = 1.0D0 INPL = .TRUE. L = 10 M = 10 N = 10 LCOMM = 2000000 CALL ZFFT3DY(0,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) CALL ZFFT3DY(-1,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) IY = 1 DO 20 I = 1, L DO 40 J = 1, M DO 10 K = 1, N X(I,J,K) = X(I,J,K)*EXP(-1.0D-3*DBLE(I+J+K-3)) CONTINUE CONTINUE SCALE = 1.0/DBLE(L*M*N) CALL ZFFT3DY(1,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 63 CFFT3DY Routine Documentation CFFT3DY (MODE,SCALE,INPL,L,M,N,X, [SUBROUTINE] INCX1,INCX2,INCX3,Y,INCY1,INCY2,INCY3,COMM,LCOMM,INFO ) [Input] The value of MODE on input determines the operation performed by CFFT3DY. On input: • MODE=0 : only initializations (specific to the values of L, M and N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=−1 : a forward 3D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT3DY. • MODE=1 : a backward (reverse) 3D transform is performed. Initializations are assumed to have been performed by a prior call to CFFT3DY. • MODE=−2 : (default) initializations and a forward 3D transform are performed. • MODE=2 : (default) initializations and a backward 3D transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the values of L, M and M ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the values of L, M and N. INTEGER MODE [Input] On input: SCALE is the scaling factor to apply to the output sequences REAL SCALE [Input] On input: if INPL is .TRUE. then X is overwritten by the output sequences; otherwise the output sequences are returned in Y. LOGICAL INPL INTEGER L [Input] On input: L is the first dimension of the 3D transform. INTEGER M [Input] On input: M is the second dimension of the 3D transform. INTEGER N [Input] On input: N is the third dimension of the 3D transform. [Input/Output] On input: X contains the L by M by N complex 3D data array to be transformed; the (ijk)th element is stored in X(1+(i-1)*INCX1+(j-1)*INCX2+(k1)*INCX3). On output: if INPL is .TRUE. then X contains the transformed data in the same locations as on input. If INPL is .FALSE. X remains unchanged. COMPLEX X(*) Chapter 5: Fast Fourier Transforms (FFTs) 64 [Input] On input: INCX1 is the step in index of X between successive data elements in the first dimension of the 3D data. Usually INCX1=1 so that succesive elements in the first dimension are stored contiguously. Constraint: INCX1 > 0. INTEGER INCX1 [Input] On input: INCX2 is the step in index of X between successive data elements in the second dimension of the 3D data. For completely contiguous data (no gaps in X ) INCX2 should be set to L. Constraint: INCX2 > 0; INCX2 > (L-1)*INCX1 if max(M,N) > 1. INTEGER INCX2 [Input] On input: INCX3 is the step in index of X between successive data elements in the third dimension of the 3D data. For completely contiguous data (no gaps in X ) INCX3 should be set to L*M. Constraint: INCX3 > 0; INCX3 > (L-1)*INCX1+(M-1)*INCX2 if N > 1. INTEGER INCX3 [Output] On output: if INPL is .FALSE. then Y contains the three-dimensional transformed data. If LTRANS=.TRUE. then the the (ijk)th element is stored in Y(1+(i-1)*INCY1+(j-1)*INCY2+(k-1)*INCY3). If INPL is .TRUE. then Y is not referenced. COMPLEX Y(*) [Input] On input: if INPL is .FALSE. then INCY1 is the step in index of Y between successive data elements in the first dimension of the 3D transformed data. Usually INCY1=1 so that succesive elements in the first dimension are stored contiguously. If INPL is .TRUE. then INCY1 is not referenced. Constraint: If INPL is .FALSE. then INCY1 > 0. INTEGER INCY1 [Input] On input: if INPL is .FALSE. then INCY2 is the step in index of Y between successive data elements in the second dimension of the 3D transformed data. For completely contiguous data (no gaps in Y ) INCY2 should be set to L. Constraint: INCY2 > 0 if INPL is .FALSE.; INCY2 > (L-1)*INCY1, if INPL is .FALSE. and max(M,N) > 1. INTEGER INCY2 [Input] On input: if INPL is .FALSE. then INCY3 is the step in index of Y between successive data elements in the third dimension of the 3D transformed data. For completely contiguous data (no gaps in Y ) INCY3 should be set to L*M. Constraint: INCY3 > 0 if INPL is .FALSE.; INCY3 > (L-1)*INCY1+(M-1)*INCY2, if INPL is .FALSE. and N > 1. INTEGER INCY3 Chapter 5: Fast Fourier Transforms (FFTs) 65 [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence dimensions. The remainder is used as temporary store; if this is not sufficient for the requirements of the routine then temporary storage space will be dynamically allocated internally. COMPLEX COMM(LCOMM ) [Input] On input: LCOMM is the length of the communication array COMM. The amount of internal dynamic allocation of temporary storage can be reduced significantly by declaring COMM to be of length at least L*M*N + 4*(L+M+N) + 300.. Constraint: LCOMM > L*M*N + 2*(L+M+N) + 300. INTEGER LCOMM [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C C C Forward 3D FFT is performed unscaled and in-place, on the leading 10x10x10 submatrix of a larger 100x100x100 array of data. The result is transformed back with scaling. * * 10 20 * SCALE = 1.0 INPL = .TRUE. L = 10 M = 10 N = 10 LCOMM = 2000000 CALL CFFT3DY(0,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) CALL CFFT3DY(-1,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) IY = 1 DO 20 I = 1, L DO 40 J = 1, M DO 10 K = 1, N X(I,J,K) = X(I,J,K)*EXP(-0.001*REAL(I+J+K-3)) CONTINUE CONTINUE SCALE = 1.0/REAL(L*M*N) CALL CFFT3DY(1,SCALE,INPL,L,M,N,X,1,100,10000,Y,1,1,1, COMM,LCOMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 66 5.3 FFTs on real and Hermitian data sequences The routines documented here compute discrete Fourier transforms (DFTs) of sequences of real numbers or of Hermitian sequences in either single or double precision arithmetic. The DFTs are computed using a highly-efficient FFT algorithm. Hermitian sequences are represented in a condensed form that is described in Section 5.1 [Introduction to FFTs], page 24. The DFT of a real sequence results in a Hermitian sequence; the DFT of a Hermitian sequence is a real sequence. Please note that prior to Release 2.0 of ACML the routine ZDFFT/CSFFT and ZDFFTM/CSFFTM returned results that were scaled by a factor 0.5 compared with the currently returned results. Chapter 5: Fast Fourier Transforms (FFTs) 67 5.3.1 FFT of single sequences of real data DZFFT Routine Documentation DZFFT (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by DZFFT. On input: • MODE=0 : only default initializations (specific to N ) are performed; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=1 : a real transform is performed. Initializations are assumed to have been performed by a prior call to DZFFT. • MODE=2 : (default) initializations and a real transform are performed. • MODE=100 : similar to MODE=0; only initializations are performed, but first a plan is generated. This plan is chosen based on the fastest FFT computation for a subset of all possible plans. INTEGER MODE INTEGER N [Input] On input: N is the length of the real sequence X [Input/Output] On input: X contains the real sequence of length N to be transformed. On output: X contains the transformed Hermitian sequence. DOUBLE PRECISION X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. DOUBLE PRECISION COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL DZFFT(0,N,X,COMM,INFO) CALL DZFFT(1,N,X,COMM,INFO) DO 10 I = N/2+2, N X(I) = -X(I) CONTINUE CALL ZDFFT(2,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 68 SCFFT Routine Documentation SCFFT (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by SCFFT. On input: • MODE=0 : only default initializations (specific to N ) are performed; this is usually followed by calls to the same routine with MODE=−1 or 1. • MODE=1 : a real transform is performed. Initializations are assumed to have been performed by a prior call to SCFFT. • MODE=2 : (default) initializations and a real transform are performed. • MODE=100 : similar to MODE=0; only initializations are performed, but first a plan is generated. This plan is chosen based on the fastest FFT computation for a subset of all possible plans. INTEGER MODE INTEGER N [Input] On input: N is the length of the real sequence X [Input/Output] On input: X contains the real sequence of length N to be transformed. On output: X contains the transformed Hermitian sequence. REAL X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. REAL COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL SCFFT(0,N,X,COMM,INFO) CALL SCFFT(1,N,X,COMM,INFO) DO 10 I = N/2+2, N X(I) = -X(I) CONTINUE CALL CSFFT(2,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 69 5.3.2 FFT of multiple sequences of real data DZFFTM Routine Documentation DZFFTM (M,N,X,COMM,INFO ) INTEGER M [SUBROUTINE] [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the real sequences in X [Input/Output] On input: X contains the M real sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. On output: X contains the transformed Hermitian sequences. DOUBLE PRECISION X(N*M) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. DOUBLE PRECISION COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL DZFFTM(1,N,X,COMM,INFO) CALL DZFFTM(2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*X(N-I+1,2) CONTINUE CALL ZDFFTM(2,N,X(1,3),COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 70 SCFFTM Routine Documentation SCFFTM (M,N,X,COMM,INFO ) INTEGER M [SUBROUTINE] [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the real sequences in X [Input/Output] On input: X contains the M real sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. On output: X contains the transformed Hermitian sequences. REAL X(N*M) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. REAL COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL SCFFTM(1,N,X,COMM,INFO) CALL SCFFTM(2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*X(N-I+1,2) CONTINUE CALL CSFFTM(1,N,X(1,3),COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 71 5.3.3 FFT of single Hermitian sequences ZDFFT Routine Documentation ZDFFT (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by ZDFFT. On input: • MODE=0 : only initializations (specific to the values of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=1. • MODE=1 : a real transform is performed. Initializations are assumed to have been performed by a prior call to ZDFFT. • MODE=2 : (default) initializations and a real transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE INTEGER N [Input] On input: N is length of the sequence in X [Input/Output] On input: X contains the Hermitian sequence of length N to be transformed. On output: X contains the transformed real sequence. DOUBLE PRECISION X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. DOUBLE PRECISION COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL DZFFT(0,N,X,COMM,INFO) CALL DZFFT(1,N,X,COMM,INFO) DO 10 I = N/2+2, N X(I) = -X(I) CONTINUE CALL ZDFFT(2,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 72 CSFFT Routine Documentation CSFFT (MODE,N,X,COMM,INFO ) [SUBROUTINE] [Input] The value of MODE on input determines the operation performed by CSFFT. On input: • MODE=0 : only initializations (specific to the values of N ) are performed using a default plan; this is usually followed by calls to the same routine with MODE=1. • MODE=1 : a real transform is performed. Initializations are assumed to have been performed by a prior call to CSFFT. • MODE=2 : (default) initializations and a real transform are performed. • MODE=100 : similar to MODE=0; only initializations (specific to the value of N ) are performed, but these are based on a plan that is first generated by timing a subset of all possible plans and choosing the quickest (i.e. the FFT computation was timed as fastest based on the chosen plan). The plan generation phase may take a significant amount of time depending on the value of N. INTEGER MODE INTEGER N [Input] On input: N is the length of the sequence in X [Input/Output] On input: X contains the Hermitian sequence of length N to be transformed. On output: X contains the transformed real sequence. REAL X(N) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. REAL COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL SCFFT(0,N,X,COMM,INFO) CALL SCFFT(1,N,X,COMM,INFO) DO 10 I = N/2+2, N X(I) = -X(I) CONTINUE CALL CSFFT(2,N,X,COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 73 5.3.4 FFT of multiple Hermitian sequences ZDFFTM Routine Documentation ZDFFTM (M,N,X,COMM,INFO ) INTEGER M [SUBROUTINE] [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the sequences in X [Input/Output] On input: X contains the M Hermitian sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. On output: X contains the transformed real sequences. DOUBLE PRECISION X(N*M) [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. DOUBLE PRECISION COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL DZFFTM(1,N,X,COMM,INFO) CALL DZFFTM(2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*X(N-I+1,2) CONTINUE CALL ZDFFTM(1,N,X(1,3),COMM,INFO) Chapter 5: Fast Fourier Transforms (FFTs) 74 CSFFTM Routine Documentation CSFFTM (M,N,X,COMM,INFO ) INTEGER M [SUBROUTINE] [Input] On input: M is the number of sequences to be transformed. INTEGER N [Input] On input: N is the length of the sequences in X [Input/Output] On input: X contains the M Hermitian sequences of length N to be transformed. Element i of sequence j is stored in location i + (j − 1) ∗ N of X. REAL X(N*M) On output: X contains the transformed real sequences. [Input/Output] COMM is a communication array. Some portions of the array are used to store initializations for subsequent calls with the same sequence length N. The remainder is used as temporary store. REAL COMM(3*N+100) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: 10 CALL SCFFTM(1,N,X,COMM,INFO) CALL SCFFTM(2,N,X,COMM,INFO) DO 10 I = 1, N X(I,3) = X(I,1)*X(N-I+1,2) CONTINUE CALL CSFFTM(1,N,X(1,3),COMM,INFO) Chapter 6: Random Number Generators 75 6 Random Number Generators Within the context of this document, a base random number generator (BRNG) is a mathematical algorithm that, given an initial state, produces a sequence (or stream) of variates (or values) uniformly distributed over the semi-open interval (0,1]. The period of the BRNG is defined as the maximum number of values that can be generated before the sequence starts to repeat. The initial state of a BRNG is often called the seed. Note that this definition means that the value 1.0 may be returned, but the value 0.0 will not. A pseudo-random number generator (PRNG) is a BRNG that produces a stream of variates that are independent and statistically indistinguishable from a random sequence. A PRNG has several advantages over a true random number generator in that the generated sequence is repeatable, has known mathematical properties and is usually much quicker to generate. A quasi-random number generator (QRNG) is similar to a PRNG, however the variates generated are not statistically independent, rather they are designed to give a more even distribution in multidimensional space. Many books on statistics and computer science have good introductions to PRNGs and QRNGs, see for example Knuth [6] or Banks [7]. All of the BRNGs supplied in the ACML are PRNGs. In addition to standard PRNGs some applications require cryptologically secure generators. A PRNG is said to be cryptologically secure if there is no polynomial-time algorithm which, on input of the first l bits of the output sequence can predict the (l + 1)st bit of the sequence with probability significantly greater than 0.5. This is equivalent to saying there exists no polynomial-time algorithm that can correctly distinguish between an output sequence from the PRNG and a truly random sequence of the same length with probability significantly greater than 0.5 [8]. A distribution generator is a routine that takes variates generated from a BRNG and transforms them into variates from a specified distribution, for example the Gaussian (Normal) distribution. The ACML contains five base generators, (Section 6.1 [Base Generators], page 75), and twenty-three distribution generators (Section 6.3 [Distribution Generators], page 97). In addition users can supply a custom built generator as the base generator for all of the distribution generators (Section 6.1.8 [User Supplied Generators], page 86). The base generators were tested using the Big Crush, Small Crush and Pseudo Diehard test suites from the TestU01 software library [15]. 6.1 Base Generators The five base generators (BRNGs) supplied with the ACML are; the NAG basic generator [9], a series of Wichmann-Hill generators [10], the Mersenne Twister [11], L’Ecuyer’s combined recursive generator MRG32k3a [12] and the Blum-Blum-Shub generator [8]. Some of the generators have been slightly modified from their usual form to make them consistent between themselves. For instance, the Wichmann-Hill generators in standard form may return exactly 0.0 but not exactly 1.0. In ACML we return 1.0 − x to convert the value x into the semi-open interval (0, 1] without affecting any other randomness properties. The original Mersenne Twister algorithm returns an exact zero about one time in a few billion; the ACML implementation returns a tiny non-zero number as surrogate for zero. Chapter 6: Random Number Generators 76 If a single stream of variates is required it is recommended that the Mersenne Twister (Section 6.1.5 [Mersenne Twister], page 84) base generator is used. This generator combines speed with good statistical properties and an extremely long period. The NAG basic generator (Section 6.1.3 [Basic NAG Generator], page 83) is another quick generator suitable for generating a single stream. However it has a shorter period than the Mersenne Twister and being a linear congruential generator, its statistical properties are not as good. If 273 or fewer multiple streams, with a period of up to 280 are required then it is recommended that the Wichmann-Hill generators are used (Section 6.1.4 [Wichmann-Hill Generator], page 84). For more streams or multiple streams with a longer period it is recommended that the L’Ecuyer combined recursive generator (Section 6.1.6 [L’Ecuyer’s Combined Recursive Generator], page 85) is used in combination with the skip ahead routine (Section 6.2.3 [Skip Ahead], page 91). Generating multiple streams of variates by skipping ahead is generally quicker than generating the streams using the leap frog method. More details on multiple streams can be found in Section 6.2 [Multiple Streams], page 90. The Blum-Blum-Shub generator (Section 6.1.7 [Blum-Blum-Shub Generator], page 85) should only be used if a cryptologically secure generator is required. This generator is extremely slow and has poor statistical properties when used as a base generator for any of the distributional generators. 6.1.1 Initialization of the Base Generators A random number generator must be initialized before use. Three routines are supplied within the ACML for this purpose: DRANDINITIALIZE, DRANDINITIALIZEBBS and DRANDINITIALIZEUSER (see [DRANDINITIALIZE], page 78, [DRANDINITIALIZEBBS], page 81 and [DRANDINITIALIZEUSER], page 87, respectively). Of these, DRANDINITIALIZE is used to initialize all of the supplied base generators, DRANDINITIALIZEBBS supplies an alternative interface to DRANDINITIALIZE for the Blum-Blum-Shub generator, and DRANDINITIALIZEUSER allows the user to register and initialize their own base generator. Both double and single precision versions of all RNG routines are supplied. Double precision names are prefixed by DRAND, and single precision by SRAND. Note that if a generator has been initialized using the relevant double precision routine, then the double precision versions of the distribution generators must also be used, and vice versa. This even applies to generators with no double or single precision parameters; for example, a call of DRANDDISCRETEUNIFORM must be preceded by a call to one of the double precision initializers (typically DRANDINITIALIZE). No utilities for saving, retrieving or copying the current state of a generator have been provided. All of the information on the current state of a generator (or stream, if multiple streams are being used) is stored in the integer array STATE and as such this array can be treated as any other integer array, allowing for easy copying, restoring etc. The statistical properties of a sequence of random numbers are only guaranteed within the sequence, and not between sequences provided by the same generator. Therefore it is likely that repeated initialization will render the numbers obtained less, rather than more, independent. In most cases there should only be a single call to one of the initialization routines, per application, and this call must be made before any variates are generated. One example of where multiple initialization may be required is briefly touched upon in Section 6.2 [Multiple Streams], page 90. Chapter 6: Random Number Generators 77 In order to initialize the Blum-Blum-Shub generator a number of additional parameters, as well as an initial state (seed), are required. Although this generator can be initialized through the DRANDINITIALIZE routine it is recommended that the DRANDINITIALIZEBBS routine is used instead. Chapter 6: Random Number Generators 78 DRANDINITIALIZE / SRANDINITIALIZE Initialize one of the five supplied base generators; NAG basic generator, Wichmann-Hill generator, Mersenne Twister, L’Ecuyer’s combined recursive generator (MRG32k3a) or the Blum-Blum-Shub generator. (Note that SRANDINITIALIZE is the single precision version of DRANDINITIALIZE. The argument lists of both routines are identical except that any double precision arguments of DRANDINITIALIZE are replaced in SRANDINITIALIZE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDINITIALIZE (GENID,SUBID,SEED,LSEED,STATE, LSTATE,INFO ) [SUBROUTINE] [Input] On input: a numerical code indicating which of the five base generators to initialize. • 1 = NAG basic generator (Section 6.1.3 [Basic NAG Generator], page 83). • 2 = Wichmann-Hill generator (Section 6.1.4 [Wichmann-Hill Generator], page 84). • 3 = Mersenne Twister (Section 6.1.5 [Mersenne Twister], page 84). • 4 = L’Ecuyer’s Combined Recursive generator (Section 6.1.6 [L’Ecuyer’s Combined Recursive Generator], page 85). • 5 = Blum-Blum-Shub generator (Section 6.1.7 [Blum-Blum-Shub Generator], page 85). INTEGER GENID Constraint: 1≤ GENID ≤ 5. [Input] On input: if GENID = 2, then SUBID indicates which of the 273 WichmannHill generators to use. If GENID = 5 then SUBID indicates the number of bits to use (v) from each of iteration of the Blum-Blum-Shub generator. In all other cases SUBID is not referenced. Constraint: If GENID = 2 then 1≤ SUBID ≤ 273 . INTEGER SUBID [Input] On input: if GENID6= 5 , then SEED is a vector of initial values for the base generator. These values must be positive integers. The number of values required depends on the base generator being used. The NAG basic generator requires one initial value, the Wichmann-Hill generator requires four initial values, the L’Ecuyer combined recursive generator requires six initial values and the Mersenne Twister requires 624 initial values. If the number of seeds required by the chosen generator is > LSEED then SEED(1) is used to initialize the NAG basic generator. This is then used to generate all of the remaining seed values required. In general it is best not to set all the elements of SEED to anything too obvious, such as a single repeated value or a simple sequence. Using such a seed array may lead to several similar values being created in a row when the generator is subsequently called. This is particularly true for the Mersenne Twister generator. INTEGER SEED(LSEED ) Chapter 6: Random Number Generators 79 In order to initialize the Blum-Blum-Shub generator two large prime values, p and q are required as well as an initial value s. As p, q and s can be of an arbitrary size, these values are expressed as a polynomial in B, where B = 224 . For example, p can be factored into a polynomial of order lp , with p = p1 + p2 B + p3 B 2 + · · · + plp B lp −1 . The elements of SEED should then be set to the following: • SEED(1) = lp • SEED(2) to SEED(lp + 1) = p1 to plp • SEED(lp + 2) = lq • SEED(lp + 3) to SEED(lp + lq + 2) = q1 to qlq • SEED(lp + lq + 3) = ls • SEED(lp + lq + 4) to SEED(lp + lq + ls + 3) = s1 to sls Constraint: If GENID6= 5 then SEED(i) > 0, i = 1, 2, · · ·. If GENID = 5 then SEED must take the values described above. [Input/Output] On input: either the length of the seed vector, SEED, or a value ≤ 0 . On output: if LSEED≤ 0 on input, then LSEED is set to the number of initial values required by the selected generator, and the routine returns. Otherwise LSEED is left unchanged. INTEGER LSEED [Output] On output: the state vector required by all of the supplied distributional and base generators. INTEGER STATE(LSTATE ) [Input/Output] On input: either the length of the state vector, STATE, or a value ≤ 0 . On output: if LSTATE≤ 0 on input, then LSTATE is set to the minimum length of the state vector STATE for the base generator chosen, and the routine returns. Otherwise LSTATE is left unchanged. Constraint: LSTATE≤ 0 or the minimum length for the chosen base generator, given by: • GENID = 1: LSTATE≥ 16, • GENID = 2: LSTATE≥ 20, • GENID = 3: LSTATE≥ 633, • GENID = 4: LSTATE≥ 61, • GENID = 5: LSTATE≥ lp + lq + ls + 6, where lp , lq and ls are the order of the polynomials used to express the parameters p, q and s respectively. INTEGER LSTATE [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then either, or both of LSEED and / or LSTATE have been set to the required length for vectors SEED and STATE respectively. Of the two variables LSEED and LSTATE, only those which had an input value ≤ 0 will have been set. The STATE vector will not have been initialized. If INFO = 0 then the state vector, STATE, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Beta distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Beta distribution CALL DRANDBETA(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 80 Chapter 6: Random Number Generators 81 DRANDINITIALIZEBBS / SRANDINITIALIZEBBS Alternative initialization routine for the Blum-Blum-Shub generator. Unlike the other base generators supplied with the ACML, the Blum-Blum-Shub generator requires two additional parameters, p and q as well as an initial state, s. The parameters p, q and s can be of an arbitrary size. In order to avoid overflow these values are expressed as a polynomial in B, where B = 224 . For example, p can be factored into a polynomial of order lp , with p = p1 + p2 B + p3 B 2 + · · · + plp B lp −1 , similarly q = q1 + q2 B + q3 B 2 + · · · + qlq B lq −1 and s = s1 + s2 B + s3 B 2 + · · · + sls B ls −1 . (Note that SRANDINITIALIZEBBS is the single precision version of DRANDINITIALIZEBBS. The argument lists of both routines are identical except that any double precision arguments of DRANDINITIALIZEBBS are replaced in SRANDINITIALIZEBBS by single precision arguments - type REAL in FORTRAN or type float in C). DRANDINITIALIZEBBS (NBITS,LP,P,LQ,Q,LS,S,STATE,LSTATE, INFO ) [SUBROUTINE] [Input] On input: the number of bits, v, to use from each iteration of the BlumBlum-Shub generator. If NBITS < 1 then NBITS = 1. If NBITS > 15 then NBITS = 15. INTEGER NBITS INTEGER LP [Input] On input: the order of the polynomial used to express p (lp ). Constraint: 1 ≤ LP ≤ 25. [Input] On input: the coefficients of the polynomial used to express p. P(i) = pi , i = 1 to lp . Constraint: 0 ≤ P (i) < 224 INTEGER P(LP ) INTEGER LQ [Input] On input: the order of the polynomial used to express q (lq ). Constraint: 1 ≤ LQ ≤ 25. [Input] On input: the coefficients of the polynomial used to express q. Q(i) = qi , i = 1 to lq . Constraint: 0 ≤ Q (i) < 224 INTEGER Q(LQ ) INTEGER LS [Input] On input: the order of the polynomial used to express s (ls ). Constraint: 1 ≤ LS ≤ 25. [Input] On input: the coefficients of the polynomial used to express s. S(i) = si , i = 1 to ls . Constraint: 0 ≤ S (i) < 224 INTEGER S(LS ) [Output] On output: the initial state for the Blum-Blum-Shub generator with parameters P,Q,S and NBITS. INTEGER STATE(*) Chapter 6: Random Number Generators 82 [Input/Output] On input: either the length of the state vector, STATE, or a value ≤ 0 . On output: if LSTATE≤ 0 on input, then LSTATE is set to the minimum length of the state vector STATE for the parameters chosen, and the routine returns. Otherwise LSTATE is left unchanged. Constraint: LSTATE≤ 0 or LSTATE ≥ lp + lq + ls + 6 INTEGER LSTATE [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LSTATE has been set to the required length for the STATE vector. If INFO = 0 then the state vector, STATE, has been successfully initialized. INTEGER INFO 6.1.2 Calling the Base Generators With the exception of the Blum-Blum-Shub generator, there are no interfaces for direct access to the base generators. All of the base generators return variates uniformly distributed over the semi-open interval (0, 1]. This functionality can be accessed using the uniform distributional generator DRANDUNIFORM, with parameter A = 0.0 and parameter B = 1.0 (see [DRANDUNIFORM], page 119). The base generator used is, as usual, selected during the initialization process (see Section 6.1.1 [Initialization of the Base Generators], page 76). To directly access the Blum-Blum-Shub generator, use the routine DRANDBLUMBLUMSHUB. Chapter 6: Random Number Generators 83 DRANDBLUMBLUMSHUB / SRANDBLUMBLUMSHUB Allows direct access to the bit stream generated by the Blum-Blum-Shub generator. (Note that SRANDBLUMBLUMSHUB is the single precision version of DRANDBLUMBLUMSHUB. The argument lists of both routines are identical except that any double precision arguments of DRANDBLUMBLUMSHUB are replaced in SRANDBLUMBLUMSHUB by single precision arguments - type REAL in FORTRAN or type float in C). DRANDBLUMBLUMSHUB (N,STATE,X,INFO ) [SUBROUTINE] [Input] On input: number of variates required. The total number of bits generated is 24N. Constraint: N ≥ 0. INTEGER N [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDBLUMBLUMSHUB STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) [Output] On output: vector holding the bit stream. The least significant 24 bits of each of the X (i) contain the bit stream as generated by the Blum-Blum-Shub generator. The least significant bit of X (1) is the first bit generated, the second least significant bit of X (1) is the second bit generated etc. INTEGER X(N ) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO 6.1.3 Basic NAG Generator The NAG basic generator is a linear congruential generator (LCG) and, like all LCGs, has the form: xi = a1 xi−1 mod m1 , xi ui = , m1 where the ui , i = 1, 2, · · · form the required sequence. The NAG basic generator takes a1 = 1313 and m1 = 259 , which gives a period of approximately 257 . This generator has been part of the NAG numerical library [9] since Mark 6 and as such has been widely used. It suffers from no known problems, other than those due to the lattice structure inherent in all LCGs, and, even though the period is relatively short compared to many of the newer generators, it is sufficiently large for many practical problems. Chapter 6: Random Number Generators 84 6.1.4 Wichmann-Hill Generator The Wichmann-Hill [10] base generator uses a combination of four linear congruential generators (LCGs) and has the form: wi xi yi zi = a1 wi−1 mod m1 = a2 xi−1 mod m2 = a3 yi−1 mod m3 = a4 zi−1 mod m4 wi xi yi zi ui = ( + + + ) mod 1, m1 m2 m3 m4 where the ui , i = 1, 2, · · · form the required sequence. There are 273 sets of parameters, {ai , mi : i = 1, 2, 3, 4}, to choose from. These values have been selected so that the resulting generators are independent and have a period of approximately 280 [10]. 6.1.5 Mersenne Twister The Mersenne Twister [11] is a twisted generalized feedback shift register generator. The algorithm is as follows: • Set some arbitrary initial values x1 , x2 , · · · , xr , each consisting of w bits. • Letting 0 Iw−1 A= , aw aw−1 · · · a1 where Iw−1 is the (w − 1) × (w − 1) identity matrix and each of the ai , i = 1 to w take a value of either 0 or 1 (i.e. they can be represented as bits). Define (w:(l+1)) xi+r = (xi+s ⊕ (xi (w:(l+1)) (l:1) |xi+1 )A), (l:1) where xi |xi+1 indicates the concatenation of the most significant (upper) w − l bits of xi and the least significant (lower) l bits of xi+1 . • Perform the following operations sequentially: z z z z ui+r = xi+r ⊕ (xi+r t1 ) = z ⊕ ((z t2 ) AND m1 ) = z ⊕ ((z t3 ) AND m2 ) = z ⊕ (z t4 ) = z/(2w − 1), where t1 , t2 , t3 and t4 are integers and m1 and m2 are bit-masks and “ t” and “ t” represent a t bit shift right and left respectively, ⊕ is bit-wise exclusively or (xor) operation and “AND” is a bit-wise and operation. Chapter 6: Random Number Generators 85 The ui+r : i = 1, 2, · · · then form a pseudo-random sequence, with ui ∈ (0, 1), for all i. This implementation of the Mersenne Twister uses the following values for the algorithmic constants: w = 32 a = 0x9908b0df l = 31 r = 624 s = 397 t1 = 11 t2 = 7 t3 = 15 t4 = 18 m1 = 0x9d2c5680 m2 = 0xefc60000 where the notation 0xDD · · · indicates the bit pattern of the integer whose hexadecimal representation is DD · · ·. This algorithm has a period length of approximately 219,937 − 1 and has been shown to be uniformly distributed in 623 dimensions. 6.1.6 L’Ecuyer’s Combined Recursive Generator The base generator referred to as L’Ecuyer’s combined recursive generator is referred to as MRG32k3a in [12] and combines two multiple recursive generators: xi = a11 xi−1 + a12 xi−2 + a13 xi−3 mod m1 yi = a21 yi−1 + a22 yi−2 + a23 yi−3 mod m2 zi = xi − yi mod m1 zi ui = , m1 where the ui , i = 1, 2, · · · form the required sequence and a11 = 0, a12 = 1403580, a13 = 810728, m1 = 232 − 209, a21 = 527612, a22 = 0, a23 = 1370589 and m2 = 232 − 22853. Combining the two multiple recursive generators (MRG) results in sequences with better statistical properties in high dimensions and longer periods compared with those generated from a single MRG. The combined generator described above has a period length of approximately 2191 6.1.7 Blum-Blum-Shub Generator The Blum-Blum-Shub pseudo random number generator is cryptologically secure under the assumption that the quadratic residuosity problem is intractable [8]. The algorithm consists of the following: • Generate two large and distinct primes, p and q, each congruent to 3 mod 4. Define m = pq. • Select a seed s taking a value between 1 and m − 1, such that the greatest common divisor between s and m is 1. Chapter 6: Random Number Generators 86 • Let x0 = s2 mod m. For i = 1, 2, · · · generate: xi = x2i−1 mod m zi = v least significant bits of xi where v≥ 1 . • The bit-sequence z1 , z2 , z3 , · · · is then the output sequence used. 6.1.8 User Supplied Generators All of the distributional generators described in Section 6.3 [Distribution Generators], page 97 require a base generator which returns a uniformly distributed value in the semiopen interval (0, 1] and ACML includes several such generators (as detailed in Section 6.1 [Base Generators], page 75). However, for greater flexibility, the ACML routines allow the user to register their own base generator function. This user-supplied generator then becomes the base generator for all of the distribution generators. A user supplied generator comes in the form of two routines, one to initialize the generator and one to generate a set of uniformly distributed values in the semi-open interval (0, 1]. These two routines can be named anything, but are referred to as UINI for the initialization routine and UGEN for the generation routine in the following documentation. In order to register a user supplied generator a call to DRANDINITIALIZEUSER must be made. Once registered the generator can be accessed and used in the same manner as the ACML supplied base generators. The specifications for DRANDINTIALIZEUSER, UINI and UGEN are given below. See the ACML example programs drandinitializeuser_example.f and drandinitializeuser_c_example.c (Section 2.9 [Examples], page 17) to understand how to use these routines in ACML. Chapter 6: Random Number Generators 87 DRANDINITIALIZEUSER / SRANDINITIALIZEUSER Registers a user supplied base generator so that it can be used with the ACML distributional generators. (Note that SRANDINITIALIZEUSER is the single precision version of DRANDINITIALIZEUSER. The argument lists of both routines are identical except that any double precision arguments of DRANDINITIALIZEUSER are replaced in SRANDINITIALIZEUSER by single precision arguments - type REAL in FORTRAN or type float in C). DRANDINITIALIZEUSER (UINI,UGEN,GENID,SUBID,SEED,LSEED, STATE,LSTATE,INFO ) [SUBROUTINE] [Input] On input: routine that will be used to initialize the user supplied generator, UGEN. SUBROUTINE UINI SUBROUTINE UGEN [Input] On input: user supplied base generator. [Input] On input: parameter is passed directly to UINI. Its function therefore depends on that routine. INTEGER GENID [Input] On input: parameter is passed directly to UINI. Its function therefore depends on that routine. INTEGER SUBID [Input] On input: parameter is passed directly to UINI. Its function therefore depends on that routine. INTEGER SEED(LSEED ) [Input/Output] On input: length of the vector SEED. This parameter is passed directly to UINI and therefore its required value depends on that routine. On output: whether LSEED changes will depend on UINI. INTEGER LSEED [Output] On output: the state vector required by all of the supplied distributional generators. The value of STATE returned by UINI has some housekeeping elements appended to the end before being returned by DRANDINITIALIZEUSER. See Section 6.1.8 [User Supplied Generators], page 86 for details about the form of STATE. INTEGER STATE(LSTATE ) [Input/Output] On input: length of the vector STATE. This parameter is passed directly to UINI and therefore its required value depends on that routine. On output: whether LSTATE changes will depend on UINI. If LSTATE≤ 0 then it is assumed that a request for the required length of STATE has been made. The value of LSTATE returned from UINI is therefore adjusted to allow for housekeeping elements to be added to the end of the STATE vector. This results in the value of LSTATE returned by DRANDINITIALIZEUSER being 3 larger than that returned by UINI. INTEGER LSTATE Chapter 6: Random Number Generators 88 [Output] On output: INFO is an error indicator. DRANDINITIALIZEUSER will return a value of −6 if the value of LSTATE is between 1 and 3. Otherwise INFO is passed directly back from UINI. It is recommended that the value of INFO returned by UINI is kept consistent with the rest of the ACML, that is if INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then either, or both of LSEED and / or LSTATE have been set to the required length for vectors SEED and STATE respectively and the STATE vector has not have been initialized. If INFO = 0 then the state vector, STATE, has been successfully initialized. INTEGER INFO Example: C C Generate 100 values from the Uniform distribution using a user supplied base generator INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,NSKIP,SEED(1),STATE(LSTATE) INTEGER X(N) DOUBLE PRECISION A,B C Set the seed SEED(1) = 1234 C Set the distributional parameters A = 0.0D0 B = 1.0D0 C C Initialize the base generator. Here ACMLRNGNB0GND is a user supplied generator and ACMLRNGNB0INI its initializer CALL DRANDINITIALIZEUSER(ACMLRNGNB0INI,ACMLRNGNB0GND,1,0,SEED, * LSEED,STATE,LSTATE,INFO) C Generate N variates from the Univariate distribution CALL DRANDUNIFORM(N,A,B,STATE,X,LDX,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) Chapter 6: Random Number Generators 89 UINI Specification for a user supplied initialization routine. UINI (GENID,SUBID,SEED,LSEED,STATE,LSTATE,INFO ) [SUBROUTINE] [Input] On input: the ID associated with the generator. It may be used for anything you like. INTEGER GENID [Input] On input: the sub-ID associated with the generator. It may be used for anything you like. INTEGER SUBID INTEGER SEED(LSEED ) [Input] On input: an array containing the initial seed for your generator. [Input/Output] On input: either the size of the SEED array, or a value < 1. On output: if LSEED < 1 on entry, LSEED must be set to the required size of the SEED array. This allows a caller of UINI to query the required size. INTEGER LSEED [Output] On output: if LSTATE < 1 on entry, STATE should be unchanged. Otherwise, STATE is a state vector holding internal details required by your generator. On exit from UINI, the array STATE must hold the following information: STATE(1) = ESTATE, where ESTATE is your minimum allowed size of array STATE. STATE(2) = MAGIC, where MAGIC is a magic number of your own choice. This can be used by your routine UGEN as a check that UINI has previously been called. STATE(3) = GENID STATE(4) = SUBID STATE(5) ... STATE(ESTATE-1) = internal state values required by your generator routine UGEN; for example, the current value of your seed. STATE(ESTATE) = MAGIC, i.e. the same value as STATE(2). INTEGER STATE(LSTATE ) [Input/Output] On input: either the size of the STATE array, or a value < 1. On output: if LSTATE < 1 on entry, LSTATE should be set to the required size of the STATE array, i.e. the value ESTATE as described above. This allows the caller of UINI to query the required size. Constraint: either LSTATE < 1 or LSTATE≥ EST AT E . INTEGER LSTATE [Output] On output: an error code, to be used in whatever way you wish; for example to flag an incorrect argument to UINI. If no error is encountered, UINI must set INFO to 0. INTEGER INFO Chapter 6: Random Number Generators 90 UGEN Specification for a user supplied base generator. UGEN (N,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: the number of random numbers to be generated. INTEGER STATE(* ) [Input/Output] On input: the internal state of your generator. [Output] On output: the array of N uniform distributed random numbers, each in the semi-open interval (0.0, 1.0] - i.e. 1.0 is a legitimate return value, but 0.0 is not. DOUBLE PRECISION X(N ) [Output] On output: a flag which you can use to signal an error in the call of UGEN - for example, if UGEN is called without being initialized by UINI. INTEGER INFO 6.2 Multiple Streams It is often advantageous to be able to generate variates from multiple, independent, streams. For example when running a simulation in parallel on several processors. There are four ways of generating multiple streams using the routines available in the ACML: • (a) Using different seeds • (b) Using different sequences • (c) Block-splitting or skipping ahead • (d) Leap frogging The four methods are detailed in the following sections. Of the four, (a) should be avoided in most cases, (b) is only really of any practical use when using the Wichmann-Hill generator, and is then still limited to 273 streams. Both block-splitting and leap-frogging work using the sequence from a single generator, both guarantee that the different sequences will not overlap and both can be scaled to an arbitrary number of streams. Leap-frogging requires no a-priori knowledge about the number of variates being generated, whereas block-splitting requires the user to know (approximately) the maximum number of variates required from each stream. Block-splitting requires no a-priori information on the number of streams required. In contrast leap-frogging requires the user to know the maximum number of streams required, prior to generating the first value. It is known that, dependent on the number of streams required, leap-frogging can lead to sequences with poor statistical properties, especially when applied to linear congruential generators (see Section 6.2.4 [Leap Frogging], page 94 for a brief explanation). In addition, for more complicated generators like a L’Ecuyer’s multiple recursive generator leap-frogging can increase the time required to generate each variate compared to block-splitting. The additional time required by block-splitting occurs at the initialization stage, and not at the variate generation stage. Therefore in most instances block-splitting would be the preferred method for generating multiple sequences. Chapter 6: Random Number Generators 91 6.2.1 Using Different Seeds A different sequence of variates can be generated from the same base generator by initializing the generator using a different set of seeds. Of the four methods for creating multiple streams described here, this is the least satisfactory. As mentioned in Section 6.1.1 [Initialization of the Base Generators], page 76, the statistical properties of the base generators are only guaranteed within sequences, not between sequences. For example, sequences generated from different starting points may overlap if the initial values are not far enough apart. The potential for overlapping sequences is reduced if the period of the generator being used is large. Although there is no guarantee of the independence of the sequences, due to its extremely large period, using the Mersenne Twister with random starting values is unlikely to lead to problems, especially if the number of sequences required is small. This is the only way in which multiple sequences can be generated with the ACML using the Mersenne Twister as the base generator. If the statistical properties of different sequences must be provable then one of the other methods should be adopted. 6.2.2 Using Different Generators Independent sequences of variates can be generated using different base generators for each sequence. For example, sequence 1 can be generated using the NAG basic generator, sequence 2 using the L’Ecuyer’s Combined Recursive generator, sequence 3 using the Mersenne Twister. The Wichmann-Hill generator implemented in the ACML is in fact a series of 273 independent generators. The particular sub-generator being used can be selected using the SUBID variable (see [DRANDINITIALIZE], page 78 for details). Therefore, in total, 277 independent streams can be generated with each using an independent generator (273 Wichmann-Hill generators, and 4 additional base generators). 6.2.3 Skip Ahead Independent sequences of variates can be generated from a single base generator through the use of block-splitting, or skipping-ahead. This method consists of splitting the sequence into k non-overlapping blocks, each of length n, where n is larger than the maximum number of variates required from any of the sequences. For example: x1 , x2 , · · · , xn , xn+1 , xn+2 , · · · , x2n , x2n+1 , x2n+2 , · · · , x3n , etc block 1 block 2 block 3 where x1 , x2 , · · · is the sequence produced by the generator of interest. Each of the k blocks provide an independent sequence. The block splitting algorithm therefore requires the sequence to be advanced a large number of places. Due to their form this can be done efficiently for linear congruential generators and multiple congruential generators. The ACML provides block-splitting for the NAG Basic generator, the Wichmann-Hill generators and L’Ecuyer’s Combined Recursive generator. Chapter 6: Random Number Generators 92 DRANDSKIPAHEAD / SRANDSKIPAHEAD Advance a generator N places. (Note that SRANDSKIPAHEAD is the single precision version of DRANDSKIPAHEAD. The argument lists of both routines are identical except that any double precision arguments of DRANDSKIPAHEAD are replaced in SRANDSKIPAHEAD by single precision arguments - type REAL in FORTRAN or type float in C). DRANDSKIPAHEAD (N,STATE,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of places to skip ahead. Constraint: N ≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDSKIPAHEAD STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: The STATE vector for a generator that has been advanced N places. Constraint: The STATE vector must be for either the NAG basic, WichmannHill or L’Ecuyer Combined Recursive base generators. INTEGER STATE(*) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators 93 Example: C C Generate 3 * 100 values from the Uniform distribution Multiple streams generated using the Skip Ahead method INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,NSKIP INTEGER SEED(1),STATE1(LSTATE),STATE2(LSTATE),STATE3(LSTATE) INTEGER X1(N),X2(N),X3(N) DOUBLE PRECISION A,B C Set the seed SEED(1) = 1234 C Set the distributional parameters A = 0.0D0 B = 1.0D0 C Initialize the STATE1 vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE1,LSTATE,INFO) C Copy the STATE1 vector into other state vectors DO 20 I = 1,LSTATE STATE2(I) = STATE1(I) STATE3(I) = STATE1(I) CONTINUE 20 C C C Calculate how many places we want to skip, this should be >> than the number of variates we wish to generate from each stream NSKIP = N * N C Advance each stream, first does not need changing CALL DRANDSKIPAHEAD(NSKIP,STATE2,INFO) CALL DRANDSKIPAHEAD(2*NSKIP,STATE3,INFO) C Generate 3 sets of N variates from the Univariate distribution CALL DRANDUNIFORM(N,A,B,STATE1,X1,LDX,INFO) CALL DRANDUNIFORM(N,A,B,STATE2,X2,LDX,INFO) CALL DRANDUNIFORM(N,A,B,STATE3,X3,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) X1(I),X2(I),X3(I) CONTINUE 40 Chapter 6: Random Number Generators 94 6.2.4 Leap Frogging Independent sequences of variates can be generated from a single base generator through the use of leap-frogging. This method involves splitting the sequence from a single generator into k disjoint subsequences. For example: Subsequence 1 : x1 , xk+1 , x2k+1 , · · · Subsequence 2 : x2 , xk+2 , x2k+2 , · · · .. . Subsequence k : xk , x2k , x3k , · · · each subsequence is then provides an independent stream. The leap-frog algorithm therefore requires the generation of every kth variate of a sequence. Due to their form this can be done efficiently for linear congruential generators and multiple congruential generators. The ACML provides leap-frogging for the NAG Basic generator, the Wichmann-Hill generators and L’Ecuyer’s Combined Recursive generator. As an illustrative example, a brief description of the algebra behind the implementation of the leap-frog algorithm (and block-splitting algorithm) for a linear congruential generator (LCG) will be given. A linear congruential generator has the form xi+1 = a1 xi mod m1 . The recursive nature of a LCG means that xi+v = a1 xi+v−1 mod m1 = a1 (a1 xi+v−2 mod m1 ) mod m1 = a21 xi+v−2 mod m1 = av1 xi mod m1 The sequence can be quickly advanced v places by multiplying the current state (xi ) by av1 mod m1 , hence allowing block-splitting. Leap-frogging is implemented by using ak1 , where k is the number of streams required, in place of a1 in the standard LCG recursive formula. In a linear congruential generator the multiplier a1 is constructed so that the generator has good statistical properties in, for example, the spectral test. When using leap-frogging to construct multiple streams this multiplier is replaced with ak1 , and there is no guarantee that this new multiplier will have suitable properties especially as the value of k depends on the number of streams required and so is likely to change depending on the application. This problem can be emphasised by the lattice structure of LCGs. Note that, due to rounding, a sequence generated using leap-frogging and a sequence constructed by taking every kth value from a set of variates generated without leap-frogging may differ slightly. These differences should only affect the least significant digit. Chapter 6: Random Number Generators 95 DRANDLEAPFROG / SRANDLEAPFROG Amend a generator so that it will generate every Kth value. (Note that SRANDLEAPFROG is the single precision version of DRANDLEAPFROG. The argument lists of both routines are identical except that any double precision arguments of DRANDLEAPFROG are replaced in SRANDLEAPFROG by single precision arguments - type REAL in FORTRAN or type float in C). DRANDLEAPFROG (N,K,STATE,INFO ) INTEGER N [SUBROUTINE] [Input] On input: total number of streams being used. Constraint: N > 0. INTEGER K [Input] On input: number of the current stream Constraint: 0< K ≤ N . [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDLEAPFROG STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: The STATE vector for a generator that has been advanced K − 1 places and will return every N th value. Constraint: The STATE array must be for either the NAG basic, WichmannHill or L’Ecuyer Combined Recursive base generators. INTEGER STATE(*) [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C C Generate 3 * 100 values from the Uniform distribution Multiple streams generated using the Leap Frog method INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO INTEGER SEED(1),STATE1(LSTATE),STATE2(LSTATE),STATE3(LSTATE) INTEGER X1(N),X2(N),X3(N) DOUBLE PRECISION A,B C Set the seed SEED(1) = 1234 C Set the distributional parameters A = 0.0D0 B = 1.0D0 C Initialize the STATE1 vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE1,LSTATE,INFO) C Copy the STATE1 vector into other state vectors DO 20 I = 1,LSTATE STATE2(I) = STATE1(I) STATE3(I) = STATE1(I) CONTINUE 20 C Update each stream so they generate every 3rd value CALL DRANDLEAPFROG(3,1,STATE1,INFO) CALL DRANDLEAPFROG(3,2,STATE2,INFO) CALL DRANDLEAPFROG(3,3,STATE3,INFO) C Generate 3 sets of N variates from the Univariate distribution CALL DRANDUNIFORM(N,A,B,STATE1,X1,LDX,INFO) CALL DRANDUNIFORM(N,A,B,STATE2,X2,LDX,INFO) CALL DRANDUNIFORM(N,A,B,STATE3,X3,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) X1(I),X2(I),X3(I) CONTINUE 40 96 Chapter 6: Random Number Generators 97 6.3 Distribution Generators 6.3.1 Continuous Univariate Distributions DRANDBETA / SRANDBETA Generates a vector of random variates from a beta distribution with probability density function, f (X), where: Γ(A + B) A−1 f (X) = X (1 − X)B−1 Γ(A)Γ(B) if 0 ≤ X ≤ 1 and A, B > 0.0, otherwise f (X) = 0. (Note that SRANDBETA is the single precision version of DRANDBETA. The argument lists of both routines are identical except that any double precision arguments of DRANDBETA are replaced in SRANDBETA by single precision arguments - type REAL in FORTRAN or type float in C). DRANDBETA (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: first parameter for the distribution. Constraint: A> 0. DOUBLE PRECISION B [Input] On input: second parameter for the distribution. Constraint: B> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDBETA STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Beta distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Beta distribution CALL DRANDBETA(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 98 Chapter 6: Random Number Generators 99 DRANDCAUCHY / SRANDCAUCHY Generates a vector of random variates from a Cauchy distribution with probability density function, f (X), where: 1 f (X) = πB(1 + ( X−A )2 ) B (Note that SRANDCAUCHY is the single precision version of DRANDCAUCHY. The argument lists of both routines are identical except that any double precision arguments of DRANDCAUCHY are replaced in SRANDCAUCHY by single precision arguments - type REAL in FORTRAN or type float in C). DRANDCAUCHY (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: median of the distribution. DOUBLE PRECISION B [Input] On input: semi-quartile range of the distribution. Constraint: B≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDCAUCHY STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Cauchy distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Cauchy distribution CALL DRANDCAUCHY(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 100 Chapter 6: Random Number Generators 101 DRANDCHISQUARED / SRANDCHISQUARED Generates a vector of random variates from a χ2 distribution with probability density function, f (X), where: ν X 2 −1 e− X2 f (X) = ν ν , 2 2 ( 2 − 1)! if X > 0, otherwise f (X) = 0. Here ν is the degrees of freedom, DF. (Note that SRANDCHISQUARED is the single precision version of DRANDCHISQUARED. The argument lists of both routines are identical except that any double precision arguments of DRANDCHISQUARED are replaced in SRANDCHISQUARED by single precision arguments - type REAL in FORTRAN or type float in C). DRANDCHISQUARED (N,DF,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER DF [Input] On input: degrees of freedom of the distribution. Constraint: DF> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDCHISQUARED STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Chi-squared distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER DF DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) DF C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Chi-squared distribution CALL DRANDCHISQUARED(N,DF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 102 Chapter 6: Random Number Generators 103 DRANDEXPONENTIAL / SRANDEXPONENTIAL Generates a vector of random variates from an exponential distribution with probability density function, f (X), where: X e− A f (X) = A if X > 0, otherwise f (X) = 0. (Note that SRANDEXPONENTIAL is the single precision version of DRANDEXPONENTIAL. The argument lists of both routines are identical except that any double precision arguments of DRANDEXPONENTIAL are replaced in SRANDEXPONENTIAL by single precision arguments - type REAL in FORTRAN or type float in C). DRANDEXPONENTIAL (N,A,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: exponential parameter. Constraint: A≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDEXPONENTIAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Exponential distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Exponential distribution CALL DRANDEXPONENTIAL(N,A,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 104 Chapter 6: Random Number Generators 105 DRANDF / SRANDF Generates a vector of random variates from an F distribution, also called the Fisher’s variance ratio distribution, with probability density function, f (X), where: µ f (X) = µ )!X 2 −1 µ 2 ( µ+ν−2 2 ( µ2 − 1)!( ν2 − 1)!(1 + µX µ+ν µ ) 2 ν2 ν , if X > 0, otherwise f (X) = 0. Here µ is the first degrees of freedom, (DF1) and ν is the second degrees of freedom, (DF2). (Note that SRANDF is the single precision version of DRANDF. The argument lists of both routines are identical except that any double precision arguments of DRANDF are replaced in SRANDF by single precision arguments - type REAL in FORTRAN or type float in C). DRANDF (N,DF1,DF2,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER DF1 [Input] On input: first degrees of freedom. Constraint: DF1≥ 0. INTEGER DF2 [Input] On input: second degrees of freedom. Constraint: DF2≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDF STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the F distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER DF1,DF2 DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) DF1,DF2 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the F distribution CALL DRANDF(N,DF1,DF2,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 106 Chapter 6: Random Number Generators 107 DRANDGAMMA / SRANDGAMMA Generates a vector of random variates from a Gamma distribution with probability density function, f (X), where: X X A−1 e− B f (X) = , B A Γ(A) if X ≥ 0 and A, B > 0.0, otherwise f (X) = 0. (Note that SRANDGAMMA is the single precision version of DRANDGAMMA. The argument lists of both routines are identical except that any double precision arguments of DRANDGAMMA are replaced in SRANDGAMMA by single precision arguments - type REAL in FORTRAN or type float in C). DRANDGAMMA (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: first parameter of the distribution. Constraint: A> 0. DOUBLE PRECISION B [Input] On input: second parameter of the distribution. Constraint: B> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDGAMMA STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Gamma distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Gamma distribution CALL DRANDGAMMA(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 108 Chapter 6: Random Number Generators 109 DRANDGAUSSIAN / DRANDGAUSSIAN Generates a vector of random variates from a Gaussian distribution with probability density function, f (X), where: (X−µ)2 e− 2σ2 √ f (X) = . σ 2π Here µ is the mean, (XMU ) and σ 2 the variance, (VAR) of the distribution. (Note that SRANDGAUSSIAN is the single precision version of DRANDGAUSSIAN. The argument lists of both routines are identical except that any double precision arguments of DRANDGAUSSIAN are replaced in SRANDGAUSSIAN by single precision arguments type REAL in FORTRAN or type float in C). DRANDGAUSSIAN (N,XMU,VAR,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION XMU [Input] On input: mean of the distribution. DOUBLE PRECISION VAR [Input] On input: variance of the distribution. Constraint: VAR≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDGAUSSIAN STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Gaussian distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION XMU,VAR DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) XMU,VAR C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Gaussian distribution CALL DRANDGAUSSIAN(N,XMU,VAR,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 110 Chapter 6: Random Number Generators 111 DRANDLOGISTIC / SRANDLOGISTIC Generates a vector of random variates from a logistic distribution with probability density function, f (X), where: (X−A) e B . f (X) = (X−A) B(1 + e B )2 (Note that SRANDLOGISTIC is the single precision version of DRANDLOGISTIC. The argument lists of both routines are identical except that any double precision arguments of DRANDLOGISTIC are replaced in SRANDLOGISTIC by single precision arguments type REAL in FORTRAN or type float in C). DRANDLOGISTIC (N,A,B,STATE,X,INFO ) [SUBROUTINE] [Input] INTEGER N On input: number of variates required. Constraint: N ≥ 0. [Input] DOUBLE PRECISION A On input: mean of the distribution. DOUBLE PRECISION B On input: spread of the distribution. B = deviation of the distribution. Constraint: B> 0. √ [Input] 3σ/π where σ is the standard [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDLOGISTIC STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Logistic distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Logistic distribution CALL DRANDLOGISTIC(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 112 Chapter 6: Random Number Generators 113 DRANDLOGNORMAL / SRANDLOGNORMAL Generates a vector of random variates from a lognormal distribution with probability density function, f (X), where: (log X−µ)2 e− 2σ2 √ f (X) = , Xσ 2π if X > 0, otherwise f (X) = 0. Here µ is the mean, (XMU ) and σ 2 the variance, (VAR) of the underlying Gaussian distribution. (Note that SRANDLOGNORMAL is the single precision version of DRANDLOGNORMAL. The argument lists of both routines are identical except that any double precision arguments of DRANDLOGNORMAL are replaced in SRANDLOGNORMAL by single precision arguments - type REAL in FORTRAN or type float in C). DRANDLOGNORMAL (N,XMU,VAR,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION XMU [Input] On input: mean of the underlying Gaussian distribution. DOUBLE PRECISION VAR [Input] On input: variance of the underlying Gaussian distribution. Constraint: VAR≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDLOGNORMAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Lognormal distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION XMU,VAR DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) XMU,VAR C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Lognormal distribution CALL DRANDLOGNORMAL(N,XMU,VAR,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 114 Chapter 6: Random Number Generators 115 DRANDSTUDENTST / SRANDSTUDENTST Generates a vector of random variates from a Students T distribution with probability density function, f (X), where: f (X) = (ν−1) ! 2 √ ( ν2 )! πν(1 + X 2 (ν+1) ) 2 ν . Here ν is the degrees of freedom, DF. (Note that SRANDSTUDENTST is the single precision version of DRANDSTUDENTST. The argument lists of both routines are identical except that any double precision arguments of DRANDSTUDENTST are replaced in SRANDSTUDENTST by single precision arguments - type REAL in FORTRAN or type float in C). DRANDSTUDENTST (N,DF,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER DF [Input] On input: degrees of freedom. Constraint: DF> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDSTUDENTST STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Students T distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER DF DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) DF C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Students T distribution CALL DRANDSTUDENTST(N,DF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 116 Chapter 6: Random Number Generators 117 DRANDTRIANGULAR / SRANDTRIANGULAR Generates a vector of random variates from a Triangular distribution with probability density function, f (X), where: f (X) = 2(X − XMIN ) , (XMAX − XMIN )(XMED − XMIN ) if XMIN < X ≤ XMED , else f (X) = 2(XMAX − X) , (XMAX − XMIN )(XMAX − XMED ) if XMED < X ≤ XMAX , otherwise f (X) = 0. (Note that SRANDTRIANGULAR is the single precision version of DRANDTRIANGULAR. The argument lists of both routines are identical except that any double precision arguments of DRANDTRIANGULAR are replaced in SRANDTRIANGULAR by single precision arguments - type REAL in FORTRAN or type float in C). DRANDTRIANGULAR (N,XMIN,XMED,XMAX,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION XMIN [Input] On input: minimum value for the distribution. DOUBLE PRECISION XMED [Input] On input: median value for the distribution. Constraint: XMIN ≤ XMED ≤ XMAX. DOUBLE PRECISION XMAX [Input] On input: maximum value for the distribution. Constraint: XMAX≥ XMIN . [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDTRIANGULAR STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Triangular distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION XMIN,XMAX,XMED DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) XMIN,XMAX,XMED C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Triangular distribution CALL DRANDTRIANGULAR(N,XMIN,XMAX,XMED,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 118 Chapter 6: Random Number Generators 119 DRANDUNIFORM / SRANDUNIFORM Generates a vector of random variates from a Uniform distribution with probability density function, f (X), where: 1 f (X) = . B−A (Note that SRANDUNIFORM is the single precision version of DRANDUNIFORM. The argument lists of both routines are identical except that any double precision arguments of DRANDUNIFORM are replaced in SRANDUNIFORM by single precision arguments - type REAL in FORTRAN or type float in C). DRANDUNIFORM (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: minimum value for the distribution. DOUBLE PRECISION B [Input] On input: maximum value for the distribution. Constraint: B≥ A. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDUNIFORM STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Uniform distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Uniform distribution CALL DRANDUNIFORM(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 120 Chapter 6: Random Number Generators 121 DRANDVONMISES / SRANDVONMISES Generates a vector of random variates from a Von Mises distribution with probability density function, f (X), where: eκ cos X f (X) = 2πI0 (κ) where X is reduced modulo 2π so that it lies between ±π, and κ is the concentration parameter VK. (Note that SRANDVONMISES is the single precision version of DRANDVONMISES. The argument lists of both routines are identical except that any double precision arguments of DRANDVONMISES are replaced in SRANDVONMISES by single precision arguments - type REAL in FORTRAN or type float in C). DRANDVONMISES (N,VK,,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION VK [Input] On input: concentration parameter. Constraint: VK> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDVONMISES STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Von Mises distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION VK DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) VK C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Von Mises distribution CALL DRANDVONMISES(N,VK,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 122 Chapter 6: Random Number Generators 123 DRANDWEIBULL / SRANDWEIBULL Generates a vector of random variates from a Weibull distribution with probability density function, f (X), where: XA AX A−1 e− B f (X) = , B if X > 0, otherwise f (X) = 0. (Note that SRANDWEIBULL is the single precision version of DRANDWEIBULL. The argument lists of both routines are identical except that any double precision arguments of DRANDWEIBULL are replaced in SRANDWEIBULL by single precision arguments - type REAL in FORTRAN or type float in C). DRANDWEIBULL (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION A [Input] On input: shape parameter for the distribution. Constraint: A> 0. DOUBLE PRECISION B [Input] On input: scale parameter for the distribution. Constraint: B> 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDWEIBULL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Weibull distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION A,B DOUBLE PRECISION X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Weibull distribution CALL DRANDWEIBULL(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 124 Chapter 6: Random Number Generators 125 6.3.2 Discrete Univariate Distributions DRANDBINOMIAL / SRANDBINOMIAL Generates a vector of random variates from a Binomial distribution with probability, f (X), defined by: M !P X (1 − P )(M −X) f (X) = , X = 0, 1, · · · , M X!(M − 1)! (Note that SRANDBINOMIAL is the single precision version of DRANDBINOMIAL. The argument lists of both routines are identical except that any double precision arguments of DRANDBINOMIAL are replaced in SRANDBINOMIAL by single precision arguments type REAL in FORTRAN or type float in C). DRANDBINOMIAL (N,M,P,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of trials. Constraint: M ≥ 0. DOUBLE PRECISION P [Input] On input: probability of success. Constraint: 0≤ P < 1. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDBINOMIAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Binomial distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER M DOUBLE PRECISION P INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Binomial distribution CALL DRANDBINOMIAL(N,M,P,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 126 Chapter 6: Random Number Generators 127 DRANDGEOMETRIC / SRANDGEOMETRIC Generates a vector of random variates from a Geometric distribution with probability, f (X), defined by: f (X) = P (1 − P )(X−1) , X = 1, 2, · · · (Note that SRANDGEOMETRIC is the single precision version of DRANDGEOMETRIC. The argument lists of both routines are identical except that any double precision arguments of DRANDGEOMETRIC are replaced in SRANDGEOMETRIC by single precision arguments - type REAL in FORTRAN or type float in C). DRANDGEOMETRIC (N,P,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. DOUBLE PRECISION P [Input] On input: distribution parameter. Constraint: 0≤ P < 1. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDGEOMETRIC STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Geometric distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION P INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Geometric distribution CALL DRANDGEOMETRIC(N,P,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 128 Chapter 6: Random Number Generators 129 DRANDHYPERGEOMETRIC / SRANDHYPERGEOMETRIC Generates a vector of random variates from a Hypergeometric distribution with probability, f (X), defined by: f (X) = s!m!(p − s)!(p − m)! , X!(s − X)!(m − X)!(p − m − s + X)!p! if X = max(0, m + s − p), · · · , min(l, m), otherwise f (X) = 0. Here p is the size of the population, (NP), s is the size of the sample taken from the population, (NS) and m is the number of labeled, or specified, items in the population, (M ). (Note that SRANDHYPERGEOMETRIC is the single precision version of DRANDHYPERGEOMETRIC. The argument lists of both routines are identical except that any double precision arguments of DRANDHYPERGEOMETRIC are replaced in SRANDHYPERGEOMETRIC by single precision arguments - type REAL in FORTRAN or type float in C). DRANDHYPERGEOMETRIC (N,NP,NS,M,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER NP [Input] On input: size of population. Constraint: NP≥ 0. INTEGER NS [Input] On input: size of sample being taken from population. Constraint: 0≤ NS ≤ NP. INTEGER M [Input] On input: number of specified items in the population. Constraint: 0≤ M ≤ NP. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDHYPERGEOMETRIC STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Hypergeometric distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER NP,NS,M INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) NP,NS,M C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Hypergeometric distribution CALL DRANDHYPERGEOMETRIC(N,NP,NS,M,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 130 Chapter 6: Random Number Generators 131 DRANDNEGATIVEBINOMIAL / SRANDNEGATIVEBINOMIAL Generates a vector of random variates from a Negative Binomial distribution with probability f (X) defined by: f (X) = (M + X − 1)!P X (1 − P )M , X = 0, 1, · · · X!(M − 1)! (Note that SRANDNEGATIVEBINOMIAL is the single precision version of DRANDNEGATIVEBINOMIAL. The argument lists of both routines are identical except that any double precision arguments of DRANDNEGATIVEBINOMIAL are replaced in SRANDNEGATIVEBINOMIAL by single precision arguments - type REAL in FORTRAN or type float in C). DRANDNEGATIVEBINOMIAL (N,M,P,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of failures. Constraint: M ≥ 0. DOUBLE PRECISION P [Input] On input: probability of success. Constraint: 0≤ P < 1. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDNEGATIVEBINOMIAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Negative Binomial distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER M DOUBLE PRECISION P INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Negative Binomial distribution CALL DRANDNEGATIVEBINOMIAL(N,M,P,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 132 Chapter 6: Random Number Generators 133 DRANDPOISSON / SRANDPOISSON Generates a vector of random variates from a Poisson distribution with probability f (X) defined by: λX e−λ f (X) = , X = 0, 1, · · · , X! where λ is the mean of the distribution, LAMBDA. (Note that SRANDPOISSON is the single precision version of DRANDPOISSON. The argument lists of both routines are identical except that any double precision arguments of DRANDPOISSON are replaced in SRANDPOISSON by single precision arguments - type REAL in FORTRAN or type float in C). DRANDPOISSON (N,LAMBDA,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of failures. Constraint: M ≥ 0. DOUBLE PRECISION LAMBDA [Input] On input: mean of the distribution. Constraint: LAMBDA≥ 0. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDPOISSON STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Poisson distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION LAMBDA INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) LAMBDA C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Poisson distribution CALL DRANDPOISSON(N,LAMBDA,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 134 Chapter 6: Random Number Generators 135 DRANDDISCRETEUNIFORM / SRANDDISCRETEUNIFORM Generates a vector of random variates from a Uniform distribution with probability f (X) defined by: 1 f (X) = , X = A, A + 1, · · · , B (B − A) (Note that SRANDDISCRETEUNIFORM is the single precision version of DRANDDISCRETEUNIFORM. The argument lists of both routines are identical except that any double precision arguments of DRANDDISCRETEUNIFORM are replaced in SRANDDISCRETEUNIFORM by single precision arguments - type REAL in FORTRAN or type float in C). DRANDDISCRETEUNIFORM (N,A,B,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER A [Input] On input: minimum for the distribution. INTEGER B [Input] On input: maximum for the distribution. Constraint: B≥ A. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDDISCRETEUNIFORM STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Uniform distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER A,B INTEGER X(N) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) A,B C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Uniform distribution CALL DRANDDISCRETEUNIFORM(N,A,B,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 136 Chapter 6: Random Number Generators 137 DRANDGENERALDISCRETE / SRANDGENERALDISCRETE Takes a reference vector initialized via one of DRANDBINOMIALREFERENCE, DRANDGEOMETRIC REFERENCE, DRANDHYPERGEOMETRICREFERENCE, DRANDNEGATIVEBINOMIALREFERENCE, DRAND POISSONREFERENCE and generates a vector of random variates from it. (Note that SRANDGENERALDISCRETE is the single precision version of DRANDGENERALDISCRETE. The argument lists of both routines are identical except that any double precision arguments of DRANDGENERALDISCRETE are replaced in SRANDGENERALDISCRETE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDGENERALDISCRETE (N,REF,STATE,X,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. [Input] On input: reference vector generated by one of the following: DRANDBINOMIALREFERENCE, DRANDGEOMETRICREFERENCE, DRANDHYPERGEOMETRICREFERENCE, DRANDNEGATIVEBINOMIALREFERENCE, DRANDPOISSONREFERENCE. DOUBLE PRECISION REF(*) [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDGENERALDISCRETE STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(N ) [Output] On output: vector of variates from the specified distribution. [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Binomial distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER M DOUBLE PRECISION P INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDBINOMIALREFERENCE(M,P,REF,LREF,INFO) C Generate N variates from the Binomial distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 138 Chapter 6: Random Number Generators 139 DRANDBINOMIALREFERENCE / SRANDBINOMIALREFERENCE Initializes a reference vector for use with DRANDGENERALDISCRETE. Reference vector is for a Binomial distribution with probability, f (X), defined by: f (X) = M !P X (1 − P )(M −X) , X = 0, 1, · · · , M X!(M − 1)! (Note that SRANDBINOMIALREFERENCE is the single precision version of DRANDBINOMIALREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDBINOMIALREFERENCE are replaced in SRANDBINOMIALREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDBINOMIALREFERENCE (M,P,REF,LREF,INFO ) INTEGER M [SUBROUTINE] [Input] On input: number of trials. Constraint: M ≥ 0. DOUBLE PRECISION P [Input] On input: probability of success. Constraint: 0≤ P < 1. [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Binomial distribution using DRANDGENERALDISCRETE. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Binomial distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER M DOUBLE PRECISION P INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDBINOMIALREFERENCE(M,P,REF,LREF,INFO) C Generate N variates from the Binomial distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 140 Chapter 6: Random Number Generators 141 DRANDGEOMETRICREFERENCE / SRANDGEOMETRICREFERENCE Initializes a reference vector for use with DRANDGENERALDISCRETE. Reference vector is for a Geometric distribution with probability, f (X), defined by: f (X) = P (1 − P )(X−1) , X = 1, 2, · · · (Note that SRANDGEOMETRICREFERENCE is the single precision version of DRANDGEOMETRICREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDGEOMETRICREFERENCE are replaced in SRANDGEOMETRICREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDGEOMETRICREFERENCE (P,REF,LREF,INFO ) DOUBLE PRECISION P [SUBROUTINE] [Input] On input: distribution parameter. Constraint: 0≤ P < 1. [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Geometric distribution using DRANDGENERALDISCRETE. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Geometric distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION P INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDGEOMETRICREFERENCE(P,REF,LREF,INFO) C Generate N variates from the Geometric distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 142 Chapter 6: Random Number Generators 143 DRANDHYPERGEOMETRICREFERENCE / SRANDHYPERGEOMETRICREFERENCE Initializes a reference vector for use with DRANDGENERALDISCRETE. Reference vector is for a Hypergeometric distribution with probability, f (X), defined by: f (X) = s!m!(p − s)!(p − m)! , X!(s − X)!(m − X)!(p − m − s + X)!p! if X = max(0, m + s − p), · · · , min(l, m), otherwise f (X) = 0. Here p is the size of the population, (NP), s is the size of the sample taken from the population, (NS) and m is the number of labeled, or specified, items in the population, (M ). (Note that SRANDHYPERGEOMETRICREFERENCE is the single precision version of DRANDHYPERGEOMETRICREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDHYPERGEOMETRICREFERENCE are replaced in SRANDHYPERGEOMETRICREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDHYPERGEOMETRICREFERENCE (NP,NS,M,REF,LREF,INFO ) INTEGER NP [SUBROUTINE] [Input] On input: size of population. Constraint: NP≥ 0. INTEGER NS [Input] On input: size of sample being taken from population. Constraint: 0≤ NS ≤ NP. INTEGER M [Input] On input: number of specified items in the population. Constraint: 0≤ M ≤ NP. [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Hypergeometric distribution using DRANDGENERALDISCRETE. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Hypergeometric distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER NP, NS,M INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) NP, NS,M C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDHYPERGEOMETRICREFERENCE(NP, NS,M,REF,LREF,INFO) C Generate N variates from the Hypergeometric distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 144 Chapter 6: Random Number Generators 145 DRANDNEGATIVEBINOMIALREFERENCE / SRANDNEGATIVEBINOMIALREFERENCE Initializes a reference vector for use with DRANDGENERALDISCRETE. Reference vector is for a Negative Binomial distribution with probability f (X) defined by: f (X) = (M + X − 1)!P X (1 − P )M , X = 0, 1, · · · X!(M − 1)! (Note that SRANDNEGATIVEBINOMIALREFERENCE is the single precision version of DRANDNEGATIVEBINOMIALREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDNEGATIVEBINOMIALREFERENCE are replaced in SRANDNEGATIVEBINOMIALREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDNEGATIVEBINOMIALREFERENCE (M,P,REF,LREF,INFO ) INTEGER M [SUBROUTINE] [Input] On input: number of failures. Constraint: M ≥ 0. DOUBLE PRECISION P [Input] On input: probability of success. Constraint: 0≤ P < 1. [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Negative Binomial distribution using DRANDGENERALDISCRETE. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Negative Binomial distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) INTEGER M DOUBLE PRECISION P INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,P C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDNEGATIVEBINOMIALREFERENCE(M,P,REF,LREF,INFO) C Generate N variates from the Negative Binomial distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 146 Chapter 6: Random Number Generators 147 DRANDPOISSONREFERENCE / SRANDPOISSONREFERENCE Initializes a reference vector for use with DRANDGENERALDISCRETE. Reference vector is for a Poisson distribution with probability f (X) defined by: f (X) = λX e−λ , X = 0, 1, · · · , X! where λ is the mean of the distribution, LAMBDA. (Note that SRANDPOISSONREFERENCE is the single precision version of DRANDPOISSONREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDPOISSONREFERENCE are replaced in SRANDPOISSONREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDPOISSONREFERENCE (LAMBDA,REF,LREF,INFO ) INTEGER M [SUBROUTINE] [Input] On input: number of failures. Constraint: M ≥ 0. DOUBLE PRECISION LAMBDA [Input] On input: mean of the distribution. Constraint: LAMBDA≥ 0. [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Poisson distribution using DRANDGENERALDISCRETE. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C Generate 100 values from the Poisson distribution INTEGER LSTATE,N PARAMETER (LSTATE=16,N=100) INTEGER I,INFO,SEED(1),STATE(LSTATE) DOUBLE PRECISION LAMBDA INTEGER X(N) INTEGER LREF DOUBLE PRECISION REF(1000) C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) LAMBDA C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDPOISSONREFERENCE(LAMBDA,REF,LREF,INFO) C Generate N variates from the Poisson distribution CALL DRANDGENERALDISCRETE(N,REF,STATE,X,INFO) C Print the results WRITE(6,*) (X(I),I=1,N) 148 Chapter 6: Random Number Generators 149 6.3.3 Continuous Multivariate Distributions DRANDMULTINORMAL / SRANDMULTINORMAL Generates an array of random variates from a Multivariate Normal distribution with probability density function, f (X), where: s f (X) = |C −1 | −(X−µ)T C −1 (X−µ) e , (2π)M where µ is the vector of means, XMU. (Note that SRANDMULTINORMAL is the single precision version of DRANDMULTINORMAL. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTINORMAL are replaced in SRANDMULTINORMAL by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTINORMAL (N,M,XMU,C,LDC,STATE,X,LDX,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of dimensions for the distribution. Constraint: M ≥ 1. DOUBLE PRECISION XMU(M) [Input] On input: vector of means for the distribution. DOUBLE PRECISION C(LDC,M) [Input] On input: variance / covariance matrix for the distribution. INTEGER LDC [Input] On input: leading dimension of C in the calling routine. Constraint: LDC≥ M . [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDMULTINORMAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(LDX,M) [Output] On output: matrix of variates from the specified distribution. INTEGER LDX [Input] On input: leading dimension of X in the calling routine. Constraint: LDX ≥ N . [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C C Generate 100 values from the Multivariate Normal distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C C Generate N variates from the Multivariate Normal distribution CALL DRANDMULTINORMAL(N,M,XMU,C,LDC,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 150 Chapter 6: Random Number Generators 151 DRANDMULTISTUDENTST / SRANDMULTISTUDENTST Generates an array of random variates from a Multivariate Students T distribution with probability density function, f (X), where: Γ f (X) = (ν+M ) 2 m 1 (πν) 2 Γ( ν2 ) |C| 2 (X − µ)T C −1 (X − µ) 1+ ν ) !− (ν+M 2 , where µ is the vector of means, XMU and ν is the degrees of freedom, DF. (Note that SRANDMULTISTUDENTST is the single precision version of DRANDMULTISTUDENTST. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTISTUDENTST are replaced in SRANDMULTISTUDENTST by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTISTUDENTST (N,M,DF,XMU,C,LDC,STATE,X,LDX,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of dimensions for the distribution. Constraint: M ≥ 1. INTEGER DF [Input] On input: degrees of freedom. Constraint: DF> 2. DOUBLE PRECISION XMU(M) [Input] On input: vector of means for the distribution. [Input] On input: matrix defining the variance / covariance for the distribution. The ν variance / covariance matrix is given by ν−2 C, where ν are the degrees of freedom, DF. DOUBLE PRECISION C(LDC,M) INTEGER LDC [Input] On input: leading dimension of C in the calling routine. Constraint: LDC≥ M . [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDMULTISTUDENTST STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(LDX,M) On output: matrix of variates from the specified distribution. [Output] Chapter 6: Random Number Generators INTEGER LDX 152 [Input] On input: leading dimension of X in the calling routine. Constraint: LDX ≥ N . [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Example: C C Generate 100 values from the Multivariate Students T distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M,DF DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,DF READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C C Generate N variates from the Multivariate Students T distribution CALL DRANDMULTISTUDENTST(N,M,DF,XMU,C,LDC,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 Chapter 6: Random Number Generators 153 DRANDMULTINORMALR / SRANDMULTINORMALR Generates an array of random variates from a Multivariate Normal distribution using a reference vector initialized by DRANDMULTINORMALREFERENCE. (Note that SRANDMULTINORMALR is the single precision version of DRANDMULTINORMALR. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTINORMALR are replaced in SRANDMULTINORMALR by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTINORMALR (N,REF,STATE,X,LDX,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. [Input] On input: a reference vector generated by DRANDMULTINORMALREFERENCE. DOUBLE PRECISION REF(*) [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDMULTINORMALR STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(LDX,M) [Output] On output: matrix of variates from the specified distribution. INTEGER LDX [Input] On input: leading dimension of X in the calling routine. Constraint: LDX ≥ N . [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: C C Generate 100 values from the Multivariate Normal distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) INTEGER LREF DOUBLE PRECISION REF(1000) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDMULTINORMALREFERENCE(M,XMU,C,LDC,REF,LREF,INFO) C C Generate N variates from the Multivariate Normal distribution CALL DRANDMULTINORMALR(N,REF,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 154 Chapter 6: Random Number Generators 155 DRANDMULTISTUDENTSTR / SRANDMULTISTUDENTSTR Generates an array of random variates from a Multivariate Students T distribution using a reference vector initialized by DRANDMULTISTUDENTSTREFERENCE. (Note that SRANDMULTISTUDENTSTR is the single precision version of DRANDMULTISTUDENTSTR. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTISTUDENTSTR are replaced in SRANDMULTISTUDENTSTR by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTISTUDENTSTR (N,REF,STATE,X,LDX,INFO ) INTEGER N [SUBROUTINE] [Input] On input: number of variates required. Constraint: N ≥ 0. [Input] On input: a reference vector generated by DRANDMULTISTUDENTSTREFERENCE. DOUBLE PRECISION REF(*) [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDMULTISTUDENTSTR STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) DOUBLE PRECISION X(LDX,M) [Output] On output: matrix of variates from the specified distribution. INTEGER LDX [Input] On input: leading dimension of X in the calling routine. Constraint: LDX ≥ N . [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators 156 Example: C C Generate 100 values from the Multivariate Students T distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M,DF DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) INTEGER LREF DOUBLE PRECISION REF(1000) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,DF READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDMULTISTUDENTSTREFERENCE(M,DF,XMU,C,LDC,REF,LREF,INFO) C C Generate N variates from the Multivariate Students T distribution CALL DRANDMULTISTUDENTSTR(N,REF,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 Chapter 6: Random Number Generators 157 DRANDMULTINORMALREFERENCE / SRANDMULTINORMALREFERENCE Initializes a reference vector for use with DRANDMULTINORMALR. Reference vector is for a Multivariate Normal distribution with probability density function, f (X), where: s f (X) = |C −1 | −(X−µ)T C −1 (X−µ) e , (2π)M where µ is the vector of means, XMU. (Note that SRANDMULTINORMALREFERENCE is the single precision version of DRANDMULTINORMALREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTINORMALREFERENCE are replaced in SRANDMULTINORMALREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTINORMALREFERENCE (M,XMU,C,LDC,REF,LREF,INFO ) INTEGER M [SUBROUTINE] [Input] On input: number of dimensions for the distribution. Constraint: M ≥ 1. DOUBLE PRECISION XMU(M) [Input] On input: vector of means for the distribution. DOUBLE PRECISION C(LDC,M) [Input] On input: variance / covariance matrix for the distribution. INTEGER LDC [Input] On input: leading dimension of C in the calling routine. Constraint: LDC≥ M . [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Multivariate Normal distribution using DRANDMULTINORMALR. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Chapter 6: Random Number Generators Example: C C Generate 100 values from the Multivariate Normal distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) INTEGER LREF DOUBLE PRECISION REF(1000) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDMULTINORMALREFERENCE(M,XMU,C,LDC,REF,LREF,INFO) C C Generate N variates from the Multivariate Normal distribution CALL DRANDMULTINORMALR(N,REF,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 158 Chapter 6: Random Number Generators 159 DRANDMULTISTUDENTSTREFERENCE / SRANDMULTISTUDENTSTREFERENCE Initializes a reference vector for use with DRANDMULTISTUDENTSTR. Reference vector is for a Multivariate Students T distribution with probability density function, f (X), where: Γ f (X) = (ν+M ) 2 m 1 (πν) 2 Γ( ν2 ) |C| 2 (X − µ)T C −1 (X − µ) 1+ ν ) !− (ν+M 2 , where µ is the vector of means, XMU and ν is the degrees of freedom, DF. (Note that SRANDMULTISTUDENTSTREFERENCE is the single precision version of DRANDMULTISTUDENTSTREFERENCE. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTISTUDENTSTREFERENCE are replaced in SRANDMULTISTUDENTSTREFERENCE by single precision arguments - type REAL in FORTRAN or type float in C). DRANDMULTISTUDENTSREFERENCE (M,DF,XMU,C,LDC,REF,LREF,INFO ) INTEGER M [SUBROUTINE] [Input] On input: number of dimensions for the distribution. Constraint: M ≥ 1. INTEGER DF [Input] On input: degrees of freedom. Constraint: DF> 2. DOUBLE PRECISION XMU(M) [Input] On input: vector of means for the distribution. [Input] On input: matrix defining the variance / covariance for the distribution. The ν variance / covariance matrix is given by ν−2 C, where ν are the degrees of freedom, DF. DOUBLE PRECISION C(LDC,M) INTEGER LDC [Input] On input: leading dimension of C in the calling routine. Constraint: LDC≥ M . [Output] On output: if INFO returns with a value of 0 then REF contains reference information required to generate values from a Multivariate Students T distribution using DRANDMULTISTUDENTSTR. DOUBLE PRECISION REF(LREF) [Input/Output] On input: either the length of the reference vector REF, or −1. On output: if LREF = −1 on input, then LREF is set to the recommended length of the reference vector and the routine returns. Otherwise LREF is left unchanged. INTEGER LREF Chapter 6: Random Number Generators 160 [Output] On output: INFO is an error indicator. If INFO = −i on exit, the i-th argument had an illegal value. If INFO = 1 on exit, then LREF has been set to the recommended length for the reference vector REF. If INFO = 0 then the reference vector, REF, has been successfully initialized. INTEGER INFO Example: C C Generate 100 values from the Multivariate Students T distribution INTEGER LSTATE,N, MM PARAMETER (LSTATE=16,N=100,MM=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,M,DF DOUBLE PRECISION X(N,MM),XMU(MM),C(MM,MM) INTEGER LREF DOUBLE PRECISION REF(1000) C Set array sizes LDC = MM LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) M,DF READ(5,*) (XMU(I),I=1,M) DO 20 I = 1,M READ(5,*) (C(I,J),J=1,M) CONTINUE 20 C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Initialize the reference vector LREF = 1000 CALL DRANDMULTISTUDENTSTREFERENCE(M,DF,XMU,C,LDC,REF,LREF,INFO) C C Generate N variates from the Multivariate Students T distribution CALL DRANDMULTISTUDENTSTR(N,REF,STATE,X,LDX,INFO) C Print the results DO 40 I = 1,N WRITE(6,*) (X(I,J),J=1,M) CONTINUE 40 Chapter 6: Random Number Generators 161 6.3.4 Discrete Multivariate Distributions DRANDMULTINOMIAL / SRANDMULTINOMIAL Generates a matrix of random variates from a Multinomial distribution with probability, f (X), defined by: K M! Y i pX f (X) = QK i , i=1 Xi ! i=1 K where X = {X1 , X2 , · · · , XK }, P = {P1 , P2 , · · · , PK }, K i=1 Xi = 1 and i=1 Pi = 1. (Note that SRANDMULTINOMIAL is the single precision version of DRANDMULTINOMIAL. The argument lists of both routines are identical except that any double precision arguments of DRANDMULTINOMIAL are replaced in SRANDMULTINOMIAL by single precision arguments - type REAL in FORTRAN or type float in C). P P DRANDMULTINOMIAL (N,M,P,K,STATE,X,LDX,INFO ) [SUBROUTINE] INTEGER N [Input] On input: number of variates required. Constraint: N ≥ 0. INTEGER M [Input] On input: number of trials. Constraint: M ≥ 0. DOUBLE PRECISION P(K) [Input] On input: vector of probabilities for each of the K possible outcomes. P Constraint: 0 ≤ Pi ≤ 1, i = 1, 2, · · · , K, K i=1 Pi = 1. INTEGER K [Input] On input: number of possible outcomes. Constraint: K≥ 2. [Input/Output] The STATE vector holds information on the state of the base generator being used and as such its minimum length varies. Prior to calling DRANDBINOMIAL STATE must have been initialized. See Section 6.1.1 [Initialization of the Base Generators], page 76 for information on initialization of the STATE variable. On input: the current state of the base generator. On output: the updated state of the base generator. INTEGER STATE(*) INTEGER X(LDX,K) [Output] On output: matrix of variates from the specified distribution. INTEGER LDX [Input] On input: leading dimension of X in the calling routine. Constraint: LDX ≥ N . [Output] On output: INFO is an error indicator. On successful exit, INFO contains 0. If INFO = −i on exit, the i-th argument had an illegal value. INTEGER INFO Chapter 6: Random Number Generators Example: 162 C Generate 100 values from the Multinomial distribution INTEGER LSTATE,N, MK PARAMETER (LSTATE=16,N=100,MK=10) INTEGER I,J,INFO,SEED(1),STATE(LSTATE) INTEGER LDC,LDX,K,M INTEGER X(N,MK) DOUBLE PRECISION P(MK) C Set array sizes LDX = N C Set the seed SEED(1) = 1234 C Read in the distributional parameters READ(5,*) K READ(5,*) (P(I),I=1,K) C Initialize the STATE vector CALL DRANDINITIALIZE(1,1,SEED,1,STATE,LSTATE,INFO) C Generate N variates from the Multinomial distribution CALL DRANDMULTINOMIAL(N,M,P,K,STATE,X,LDX,INFO) C Print the results DO 20 I = 1,N WRITE(6,*) (X(I,J),J=1,K) 20 CONTINUE Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 163 7 ACML MV: Fast Math and Fast Vector Math Library 7.1 Introduction to ACML MV ACML MV is a library which contains fast and/or vectorized versions of some familiar math library routines such as sin, cos and exp. The routines take advantage of the AMD64 architecture for performance, and so are currently only available with 64-bit versions of ACML. The routines in the library are very accurate over the range of acceptable input arguments. Some of the performance is gained by sacrificing error handling or the acceptance of certain arguments. It is therefore the responsibility of the caller of these routines to ensure that their arguments are suitable. Furthermore, some of the routines are not callable from highlevel languages at all, but must be called via assembly language; see the documentation of individual routines for details. Hence, these routines are intended to be utilized by knowledgeable users only. 7.1.1 Terminology The individual documentation for a routine states what outputs will be returned for special arguments, and also gives an indication of performance of the routine. In general, special case arguments for any routine will cause a return value in accordance with the C99 language standard [13]. Special case arguments include NaNs and infinities, as defined by the IEEE arithmetic standard [14]. In these documents, NaN means Not a Number, QNaN means Quiet NaN, and SNaN means Signalling NaN. A denormal number is a number which is very tiny (close to the machine arithmetic underflow threshold) and is stored to less precision than a normal number. Due to their special nature, operations on such numbers are often very slow. While such numbers might not necessarily be regarded as special case arguments, for the sake of performance some of the ACML MV routines have been designed not to handle them. This has been noted in the documentation for each ACML MV routine. Performance of a routine is given in machine cycles, and is thus independent of processor speed. Accuracy of a routine is quoted in ulps, where ulp stands for Unit in the Last Place. Since floating-point numbers on a computer are limited precision approximations of mathematical numbers, not all real numbers can be represented by machine numbers, and the machine number must in general be rounded to available precision. An ulp is the distance between the two machine numbers that bracket a real number. In this document, the ulp is used as a measure of the error in a returned result when compared with the mathematically exact expected result. Because of the finite nature of machine arithmetic, a routine can never in general achieve accuracy of better than 0.5 ulps, and an accuracy of less than 1 ulp is good. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 164 7.1.2 Weak Aliases Some of the functions in ACML MV include a weak alias to an equivalent function in libm. For example, the fastcos function includes a weak alias to cos. If ACML MV is included in the link order before libm, then all calls to the aliased libm function name (e.g. cos) will use the equivalent ACML MV routine (e.g. fastcos). If ACML MV is included in the link order after libm, then all calls to libm functions will use the libm versions. ACML MV routines can always be accessed using their ACML MV names (e.g. fastcos), regardless of link order. 7.1.3 Defined Types The following types are used to describe the functions contained in this chapter: m128d a pair of double precision values; m128 four single precision values. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 165 7.2 Fast Basic Math Functions This section documents the interfaces to a set of basic mathematical functions. fastcos: fast double precision Cosine double fastcos (double x) Weak alias: cos C Prototype: double fastcos (double x); Inputs: double x - the double precision input value. Outputs: Cosine of x. Fortran Function Interface: DOUBLE PRECISION FASTCOS(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: Cosine of X. Notes: fastcos computes the Cosine function of its argument x. This is a relaxed version of or applications not requiring unpredictable results. Error accurate to better than 2 ulp Special case return values: cos, suitable for use with fastmath compiler flags full error handling. Denormal inputs may produce inputs produce C99 return values. The routine is over most of the valid input range. Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 88 cycles for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 166 fastcosf: fast single precision Cosine float fastcosf (float x) Weak alias: cosf C Prototype: float fastcosf (float x); Inputs: float x - the single precision input value. Outputs: Single precision Cosine of x. Fortran Function Interface: REAL FASTCOSF(X) Inputs: REAL X - the single precision input value. Return Value: Cosine of X. Notes: fastcosf computes the single precision Cosine function of its argument x. This is a relaxed version of cosf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over most of the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 91 cycles for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 167 fastexp: fast double precision exponential function double fastexp (double x) Weak alias: exp C Prototype: double fastexp (double x); Inputs: double x - the double precision input value. Outputs: e raised to the power x (exponential of x). Fortran Function Interface: DOUBLE PRECISION FASTEXP(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: e raised to the power X (exponential of X). Notes: fastexp computes the double precision exponential function of the input argument x. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN < −708.5 0 > 709.8 +∞ Performance: 75 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 168 fastexpf: fast single precision exponential function float fastexpf (float x) Weak alias: expf C Prototype: float fastexpf (float x); Inputs: float x - the single precision input value. Outputs: e raised to the power x (exponential of x). Fortran Function Interface: REAL FASTEXPF(X) Inputs: REAL X - the single precision input value. Return Value: e raised to the power X (exponential of X). Notes: fastexpf computes the single precision exponential function of the input argument x. This is a relaxed version of expf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN < −87.5 0 > 88 +∞ Performance: 75 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 169 fastlog: fast double precision natural logarithm function double fastlog (double x) Weak alias: log C Prototype: double fastlog (double x); Inputs: double x - the double precision input value. Outputs: The natural logarithm (base e) of x. Fortran Function Interface: DOUBLE PRECISION FASTLOG(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: The natural logarithm (base e) of X. Notes: fastlog computes the double precision natural logarithm of its argument x. This is a relaxed version of or applications not requiring unpredictable results. Error accurate to better than 1 ulp Special case return values: log, suitable for use with fastmath compiler flags full error handling. Denormal inputs may produce inputs produce C99 return values. The routine is over the valid input range. Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 97 cycles for most valid inputs. 86 cycles for .97 < x < 1.03 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 170 fastlogf: fast single precision natural logarithm function float fastlogf (float x) Weak alias: logf C Prototype: float fastlogf (float x); Inputs: float x - the single precision input value. Outputs: The natural logarithm (base e) of x. Fortran Function Interface: REAL FASTLOGF(X) Inputs: REAL X - the single precision input value. Return Value: The natural logarithm (base e) of X. Notes: fastlogf computes the single precision natural logarithm of its argument x. This is a relaxed version of logf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 94 cycles for most valid inputs. 85 cycles for .97 < x < 1.03 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 171 fastlog10: fast double precision base-10 logarithm function double fastlog10 (double x) Weak alias: log10 C Prototype: double fastlog10 (double x); Inputs: double x - the double precision input value. Outputs: The base-10 logarithm of x. Fortran Function Interface: DOUBLE PRECISION FASTLOG10(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: The base-10 logarithm of X. Notes: fastlog10 computes the double precision base-10 logarithm of its argument x. This is a relaxed version of log10, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 112 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 172 fastlog10f: fast single precision base-10 logarithm function float fastlog10f (float x) Weak alias: log10f C Prototype: float fastlog10f (float x); Inputs: float x - the single precision input value. Outputs: The base-10 logarithm of x. Fortran Function Interface: REAL FASTLOG10F(X) Inputs: REAL X - the single precision input value. Return Value: The base-10 logarithm of X. Notes: fastlog10f computes the single precision base-10 logarithm of its argument x. This is a relaxed version of log10f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 104 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 173 fastlog2: fast double precision base-2 logarithm function double fastlog2 (double x) Weak alias: log2 C Prototype: double fastlog2 (double x); Inputs: double x - the double precision input value. Outputs: The base-2 logarithm of x. Fortran Function Interface: DOUBLE PRECISION FASTLOG2(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: The base-2 logarithm of X. Notes: fastlog2 computes the double precision base-2 logarithm of its argument x. This is a relaxed version of log2, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 112 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 174 fastlog2f: fast single precision base-2 logarithm function float fastlog2f (float x) Weak alias: log2f C Prototype: float fastlog2f (float x); Inputs: float x - the single precision input value. Outputs: The base-2 logarithm of x. Fortran Function Interface: REAL FASTLOG2F(X) Inputs: REAL X - the single precision input value. Return Value: The base-2 logarithm of X. Notes: fastlog2f computes the single precision base-2 logarithm of its argument x. This is a relaxed version of log2f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 107 cycles for most valid inputs. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 175 fastpow: fast double precision power function double fastpow (double x, double y) Weak alias: pow C Prototype: double fastpow (double x, double y); Inputs: double x - the double precision base input value. double y - the double precision exponent input value. Outputs: x raised to the power y. Fortran Function Interface: DOUBLE PRECISION FASTPOW(X,Y) Inputs: DOUBLE PRECISION X - the base value. DOUBLE PRECISION Y - the exponent value. Return Value: X raised to the power Y. Notes: fastpow computes the x raised to the power y in double precision. This is a relaxed version of pow, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs will produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input x ±0 ±0 ±0 ±0 −1 +1 x (incl. Nan) x<0 |x|<1 |x|>1 |x|<1 |x|>1 −∞ −∞ −∞ −∞ Input y y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer +∞ y (incl. NaN ) ±0 y, not integer −∞ −∞ +∞ +∞ y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer Output ±∞ +∞ ±0 +0 1 1 1 QNaN +∞ +0 +0 +∞ −0 +0 −∞ +∞ Chapter 7: ACML MV: Fast Math and Fast Vector Math Library +∞ y < 0, +∞ y > 0, NaN y nonzero, x<>1 NaN, Performance: 200 cycles for most valid inputs. +0 +∞ NaN NaN 176 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 177 fastpowf: fast single precision power function float fastpowf (float x, float y) Weak alias: powf C Prototype: float fastpowf (float x, float y); Inputs: float x - the single precision base input value. float y - the single precision exponent input value. Outputs: x raised to the power y. Fortran Function Interface: REAL FASTPOWF(X,Y) Inputs: REAL X - the single precision base value. REAL Y - the single precision exponent value. Return Value: X raised to the power Y. Notes: fastpowf computes the x raised to the power y in single precision. This is a relaxed version of powf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs will produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 0.5 ulp over the valid input range. Special case return values: Input x ±0 ±0 ±0 ±0 −1 +1 x (incl. Nan) x<0 |x|<1 |x|>1 |x|<1 |x|>1 −∞ −∞ −∞ −∞ Input y y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer +∞ y (incl. NaN ) ±0 y, not integer −∞ −∞ +∞ +∞ y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer Output ±∞ +∞ ±0 +0 1 1 1 QNaN +∞ +0 +0 +∞ −0 +0 −∞ +∞ Chapter 7: ACML MV: Fast Math and Fast Vector Math Library +∞ y < 0, +∞ y > 0, NaN y nonzero, x<>1 NaN, Performance: 175 cycles for most valid inputs. +0 +∞ NaN NaN 178 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 179 fastsin: fast double precision Sine double fastsin (double x) Weak alias: sin C Prototype: double fastsin (double x); Inputs: double x - the double precision input value. Outputs: Sine of x. Fortran Function Interface: DOUBLE PRECISION FASTSIN(X) Inputs: DOUBLE PRECISION X - the double precision input value. Return Value: Sine of X. Notes: fastsin computes the Sine function of its argument x. This is a relaxed version of or applications not requiring unpredictable results. Error accurate to better than 1 ulp Special case return values: sin, suitable for use with fastmath compiler flags full error handling. Denormal inputs may produce inputs produce C99 return values. The routine is over the valid input range. Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 88 cycles for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 180 fastsinf: fast single precision Sine float fastsinf (float x) Weak alias: sinf C Prototype: float fastsinf (float x); Inputs: float x - the single precision input value. Outputs: Single precision Sine of x. Fortran Function Interface: REAL PRECISION FASTSINF(X) Inputs: REAL PRECISION X - the single precision input value. Return Value: Sine of X. Notes: fastsinf computes the Sine function of its argument x. This is a relaxed version of sinf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 88 cycles for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 181 fastsincos: fast double precision Sine and Cosine void fastsincos (double x, double *s, double *c) Weak alias: sincos C Prototype: void fastsincos (double x, double *s, double *c); Inputs: double x - the double precision input value. Outputs: double *s - Sine of x. double *c - Cosine of x. Fortran Subroutine Interface: SUBROUTINE FASTSINCOS(X,S,C) Inputs: DOUBLE PRECISION X - the double precision input value. Outputs: DOUBLE PRECISION S - Sine of X. DOUBLE PRECISION C - Cosine of X. Notes: fastsincos computes the Sine and Cosine functions of its argument x. This function can provide a significant performance advantage for applications that require both the sine and cosine of an angle, such as axis and matrix rotation. This is a relaxed version of sincos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 99 cycles for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 182 fastsincosf: fast single precision Sine and Cosine void fastsincosf (float x, float *s, float *c) Weak alias: sincosf C Prototype: void fastsincosf (float x, float *s, float *c); Inputs: float x - the single precision input value. Outputs: float *s - Sine of x. float *c - Cosine of x. Fortran Subroutine Interface: SUBROUTINE FASTSINCOSF(X,S,C) Inputs: REAL X - the single precision input value. Outputs: REAL S - Sine of X. REAL C - Cosine of X. Notes: fastsincosf computes the Sine and Cosine functions of its argument x. This function can provide a significant performance advantage for applications that require both the sine and cosine of an angle, such as axis and matrix rotation. This is a relaxed version of sincosf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Error inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 91-102 cycles Output same QNaN same NaN converted to QNaN QNaN QNaN for most valid inputs < 5e5. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 183 7.3 Fast Vector Math Functions This section documents the interfaces to a set of vector mathematical functions. vrd2 cos: Two-valued double precision Cosine __m128d __vrd2_cos ( m128d x) C Prototype: m128d vrd2 cos( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: m128d y - the double precision Cosine result pair, returned in xmm0. Notes: vrd2 cos computes the Cosine function of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision Cosine of both values, returned as a m128d value. This is a relaxed version of cos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 120 cycles for most Output same QNaN same NaN converted to QNaN QNaN QNaN valid inputs < 5e5 (60 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 184 vrd4 cos: Four-valued double precision Cosine __m128d,__m128d __vrd4_cos ( m128d x1, m128d x2) C Prototype: m128d vrd2 cos( m128d x); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: m128d y1 - the first double precision Cosine result pair, returned in xmm0. m128d y2 - second double precision Cosine result pair, returned in xmm1. Notes: vrd4 cos computes the Cosine function of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision Cosine of the four values, returned as two m128d values. This is a relaxed version of cos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 172 cycles for most Output same QNaN same NaN converted to QNaN QNaN QNaN valid inputs < 5e5 (43 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 185 vrda cos: Array double precision Cosine void vrda_cos (int n, double *x, double *y) C Prototype: void vrda cos (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: Cosine for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA COS(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of Cosines of input values. Notes: vrda cos computes the Cosine function for each element of an array of input arguments. This routine accepts an array of double precision input values, computes cos(x) for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of cos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 172 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (43 cycles per value), n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 186 vrs4 cosf: Four-valued single precision Cosine __m128 __vrs4_cosf ( m128 x) C Prototype: m128 vrs4 cosf( m128 x); Inputs: m128 x - the four single precision input values. Outputs: m128 y - the four single precision Cosine results , returned in xmm0. Notes: vrs4 cosf computes the Cosine function of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision Cosine of all four values, returned as a m128 value. This is a relaxed version of cosf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 176 cycles for most Output same QNaN same NaN converted to QNaN QNaN QNaN valid inputs < 5e5 (44 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 187 vrsa cosf: Array single precision Cosine void vrsa_cosf (int n, float *x, float *y) C Prototype: void vrsa cosf (int n, float *x, float *y) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: Cosine for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA COSF(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of Cosines of input values. Notes: vrsa cosf computes the Cosine function for each element of an array of input arguments. This routine accepts an array of single precision input values, computes cosf(x) for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of cosf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 43 cycles per value for most valid inputs < 5e5, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 188 vrd2 exp: Two-valued double precision exponential function __m128d __vrd2_exp ( m128d x) C Prototype: m128d vrd2 exp( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: e raised to the power x (exponential of x). m128d y - the double precision exponent result pair, returned in xmm0. Notes: vrd2 exp computes the exponential function of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision exponent of both values, returned as a m128d value. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN < −708.5 0 > 709.8 +∞ Performance: 80 cycles for most valid inputs (40 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 189 vrd4 exp: Four-valued double precision exponential function __m128d,__m128d __vrd4_exp ( m128d x1, Prototype: m128d, m128d m128d x2) vrd4 exp( m128d x1, m128d x2); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: m128d y1 - the first double precision exponent result pair, returned in xmm0. m128d y2 - the second double precision exponent result pair, returned in xmm1. Notes: vrd4 exp computes the double precision exponential function of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision exponent of the four values, returned as two m128d values. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN < −708.5 > 709.8 Performance: 132 cycles for most Output same QNaN same NaN converted to QNaN 0 +∞ valid inputs (33 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 190 vrda exp: Array double precision exponential function void vrda_exp (int n, double *x, double *y) C Prototype: void vrda exp (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: e raised to the power x (exponential of x) for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA EXP(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of exponentials (e raised to the power x) of input values. Notes: vrda exp computes the double precision exponential function for each element of an array of input arguments. This routine accepts an array of double precision input values, computes the ex for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN < −708.5 > 709.8 Performance: 33 cycles per value Output same QNaN same NaN converted to QNaN 0 +∞ for valid inputs, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 191 vrs4 expf: Four-valued single precision exponential function __m128 __vrs4_expf ( m128 x) C Prototype: m128 vrs4 expf( m128 x); Inputs: m128 x - the four single precision input values. Outputs: e raised to the power x (exponential of x) for each input value x. m128 y - the four single precision exponent results, returned in xmm0. Notes: vrs4 expf computes the double precision exponential function of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision exponent of the four values, returned as a m128 value. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN < −87.5 0 > 88 +∞ Performance: 91 cycles for most valid inputs (23 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 192 vrs8 expf: Eight-valued single precision exponential function __m128,__m128 __vrs8_expf ( m128 x1, Prototype: m128, m128 vrs8 expf( m128 x1, m128 x2) m128 x2); Note that this function uses a non-standard programming interface. The two m128 inputs, which contain eight single precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128 is non-standard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128 x1 - the first single precision vector of four input values. m128 x2 - the second single precision vector of four input values. Outputs: m128 y1 - the first four single precision exponent results, returned in xmm0. m128 y2 - the second four single precision exponent results, returned in xmm1. Notes: vrs8 expf computes the single precision exponential function of eight input arguments. This routine accepts eight single precision input values passed as two m128 values. The result is the single precision exponent of the eight values, returned as two m128 values. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN < −87.5 > 88 Performance: 155 cycles for most Output same QNaN same NaN converted to QNaN 0 +∞ valid inputs (19 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 193 vrsa expf: Array single precision exponential function void vrsa_expf (int n, float *x, float *y) C Prototype: void vrsa expf (int n, float *x, float *y) Inputs: int n - the number of single precision values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: e raised to the power x (exponential of x) for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA EXPF(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of exponentials (e raised to the power x) of input values. Notes: vrsa expf computes the single precision exponential function for each element of an array of input arguments. This routine accepts an array of single precision input values, computes the ex for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of exp, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN < −87.5 > 88 Performance: 15 cycles per value Output same QNaN same NaN converted to QNaN 0 +∞ for valid inputs, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 194 vrd2 log: Two-valued double precision natural logarithm __m128d __vrd2_log ( m128d x) C Prototype: m128d vrd2 log( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: The natural (base e) logarithm of x. m128d y - the double precision natural logarithm result pair, returned in xmm0. Notes: vrd2 log computes the natural logarithm for each of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision natural logarithm of both values, returned as a m128d value. This is a relaxed version of log, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 130 cycles for Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (65 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 195 vrd4 log: Four-valued double precision natural logarithm __m128d,__m128d __vrd4_log ( m128d x1, Prototype: m128d, m128d m128d x2) vrd4 log( m128d x1, m128d x2); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: The natural (base e) logarithm of x. m128d y1 - the first double precision natural logarithm result pair, returned in xmm0. m128d y2 - the second double precision natural logarithm result pair, returned in xmm1. Notes: vrd4 log computes the natural logarithm for each of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision natural logarithm of the four values, returned as two m128d values. This is a relaxed version of log, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 196 cycles for Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (49 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 196 vrda log: Array double precision natural logarithm void vrda_log (int n, double *x, double *y) C Prototype: void vrda log (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: The natural (base e) logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA LOG(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of natural (base e) logarithms of input values. Notes: vrda log computes the double precision natural logarithm for each element of an array of input arguments. This routine accepts an array of double precision input values, computes the natural log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of log, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 51 cycles per value for valid inputs, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 197 vrs4 logf: Four-valued single precision natural logarithm __m128 __vrs4_logf ( m128 x) C Prototype: m128 vrs4 logf( m128 x); Inputs: m128 x - the single precision input values. Outputs: The natural (base e) logarithm of x. m128 y - the single precision natural logarithm results, returned in xmm0. Notes: vrs4 logf computes the natural logarithm for each of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision natural logarithm of all four values, returned as a m128 value. This is a relaxed version of logf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 124 cycles for Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (31 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 198 vrs8 logf: Eight-valued single precision natural logarithm __m128,__m128 __vrs8_logf ( m128 x1, Prototype: m128, m128 vrs8 logf( m128 x1, m128 x2) m128 x2); Note that this function uses a non-standard programming interface. The two m128 inputs, which contain eight single precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128 is non-standard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128 x1 - the first single precision input value pair. m128 x2 - the second single precision input value pair. Outputs: The natural (base e) logarithm of x. m128 y1 - the first single precision natural logarithm result pair, returned in xmm0. m128 y2 - the second single precision natural logarithm result pair, returned in xmm1. Notes: vrs8 logf computes the natural logarithm for each of eight input arguments. This routine accepts eight single precision input values passed as two m128 values. The result is the single precision natural logarithm of the eight values, returned as two m128 values. This is a relaxed version of logf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 200 cycles for Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (25 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 199 vrsa logf: Array single precision natural logarithm void vrsa_logf (int n, float *x, float *y) C Prototype: void vrsa logf (int n, float *x, float *y) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: The natural (base e) logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA LOGF(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of natural (base e) logarithms of input values. Notes: vrsa logf computes the single precision natural logarithm for each element of an array of input arguments. This routine accepts an array of single precision input values, computes the natural log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of logf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output ±0 −∞ negative QNaN QNaN same QNaN SNaN same NaN converted to QNaN +∞ +∞ −∞ QNaN Performance: 26 cycles per value for valid inputs, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 200 vrd2 log10: Two-valued double precision base-10 logarithm __m128d __vrd2_log10 ( m128d x) C Prototype: m128d vrd2 log10( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: The base-10 logarithm of x. m128d y - the double precision base-10 logarithm result pair, returned in xmm0. Notes: vrd2 log10 computes the base-10 logarithm for each of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision base-10 logarithm of both values, returned as a m128d value. This is a relaxed version of log10, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 142 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (71 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 201 vrd4 log10: Four-valued double precision base-10 logarithm __m128d,__m128d __vrd4_log10 ( m128d x1, Prototype: m128d, m128d m128d x2) vrd4 log10( m128d x1, m128d x2); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: The base-10 logarithm of x. m128d y1 - the first double precision base-10 logarithm result pair, returned in xmm0. m128d y2 - the second double precision base-10 logarithm result pair, returned in xmm1. Notes: vrd4 log10 computes the base-10 logarithm for each of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision base-10 logarithm of the four values, returned as two m128d values. This is a relaxed version of log10, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 235 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (59 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 202 vrda log10: Array double precision base-10 logarithm void vrda_log10 (int n, double *x, double *y) C Prototype: void vrda log10 (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: The base-10 logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA LOG10(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of base-10 logarithms of input values. Notes: vrda log10 computes the double precision base-10 logarithm for each element of an array of input arguments. This routine accepts an array of double precision input values, computes the base10 log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of log10, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 54 cycles per 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN value for valid inputs, n = 24, longer for input values very close to Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 203 vrs4 log10f: Four-valued single precision base-10 logarithm __m128 __vrs4_log10f ( m128 x) Prototype: m128 vrs4 log10f( m128 x); Inputs: m128 x - the four single precision inputs. Outputs: The base-10 logarithm of x. m128 y - the four single precision base-10 logarithm results, returned in xmm0. Notes: vrs4 log10f computes the base-10 logarithm for each of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision base-10 logarithm of the four values, returned as a m128 value. This is a relaxed version of log10, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 141 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (35 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 204 vrs8 log10f: Eight-valued single precision base-10 logarithm __m128,__m128 __vrs8_log10f ( m128 x1, Prototype: m128, m128 vrs8 log10f( m128 x1, m128 x2) m128 x2); Note that this function uses a non-standard programming interface. The two m128 inputs, which contain eight single precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128 is non-standard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128 x1 - the first set of four single precision input values. m128 x2 - the second set of four single precision input values. Outputs: The base-10 logarithm of x. m128 y1 - the first set of four single precision base-10 logarithm results, returned in xmm0. m128 y2 - the second set of four single precision base-10 logarithm results, returned in xmm1. Notes: vrs8 log10f computes the base-10 logarithm for each of eight input arguments. This routine accepts eight single precision input values passed as two m128 values. The result is the single precision base-10 logarithm of the eight values, returned as two m128 values. This is a relaxed version of log10f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 231 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (29 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 205 vrsa log10f: Array single precision base-10 logarithm void vrsa_log10f (int n, float *x, float *y) C Prototype: void vrsa log10f (int n, float *x, float *y) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: The base-10 logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA LOG10F(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of base-10 logarithms of input values. Notes: vrsa log10f computes the single precision base-10 logarithm for each element of an array of input arguments. This routine accepts an array of single precision input values, computes the base10 log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of log10f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 28 cycles per 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN value for valid inputs, n = 24, longer for input values very close to Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 206 vrd2 log2: Two-valued double precision base-2 logarithm __m128d __vrd2_log2 ( m128d x) C Prototype: m128d vrd2 log2( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: The base-2 logarithm of x. m128d y - the double precision base-2 logarithm result pair, returned in xmm0. Notes: vrd2 log2 computes the base-2 logarithm for each of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision base-2 logarithm of both values, returned as a m128d value. This is a relaxed version of log2, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 142 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (71 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 207 vrd4 log2: Four-valued double precision base-2 logarithm __m128d,__m128d __vrd4_log2 ( m128d x1, Prototype: m128d, m128d m128d x2) vrd4 log2( m128d x1, m128d x2); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: The base-2 logarithm of x. m128d y1 - the first double precision base-2 logarithm result pair, returned in xmm0. m128d y2 - the second double precision base-2 logarithm result pair, returned in xmm1. Notes: vrd4 log2 computes the base-2 logarithm for each of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision base-2 logarithm of the four values, returned as two m128d values. This is a relaxed version of log2, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 235 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (59 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 208 vrda log2: Array double precision base-2 logarithm void vrda_log2 (int n, double *x, double *y) C Prototype: void vrda log2 (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: The base-2 logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA LOG2(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of base-2 logarithms of input values. Notes: vrda log2 computes the double precision base-2 logarithm for each element of an array of input arguments. This routine accepts an array of double precision input values, computes the base2 log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of log2, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 54 cycles per 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN value for valid inputs, n = 24, longer for input values very close to Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 209 vrs4 log2f: Four-valued single precision base-2 logarithm __m128 __vrs4_log2f ( m128 x) Prototype: m128 vrs4 log2f( m128 x); Inputs: m128 x - the four single precision inputs. Outputs: The base-2 logarithm of x. m128 y - the four single precision base-2 logarithm results, returned in xmm0. Notes: vrs4 log2f computes the base-2 logarithm for each of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision base-2 logarithm of the four values, returned as a m128 value. This is a relaxed version of log2, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 141 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (35 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 210 vrs8 log2f: Eight-valued single precision base-2 logarithm __m128,__m128 __vrs8_log2f ( m128 x1, Prototype: m128, m128 vrs8 log2f( m128 x1, m128 x2) m128 x2); Note that this function uses a non-standard programming interface. The two m128 inputs, which contain eight single precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128 is non-standard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128 x1 - the first set of four single precision input values. m128 x2 - the second set of four single precision input values. Outputs: The base-2 logarithm of x. m128 y1 - the first set of four single precision base-2 logarithm results, returned in xmm0. m128 y2 - the second set of four single precision base-2 logarithm results, returned in xmm1. Notes: vrs8 log2f computes the base-2 logarithm for each of eight input arguments. This routine accepts eight single precision input values passed as two m128 values. The result is the single precision base-2 logarithm of the eight values, returned as two m128 values. This is a relaxed version of log2f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 203 cycles for close to 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN most valid inputs (25 cycles per value), longer for input values very Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 211 vrsa log2f: Array single precision base-2 logarithm void vrsa_log2f (int n, float *x, float *y) C Prototype: void vrsa log2f (int n, float *x, float *y) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: The base-2 logarithm of each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA LOG2F(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of base-2 logarithms of input values. Notes: vrsa log2f computes the single precision base-2 logarithm for each element of an array of input arguments. This routine accepts an array of single precision input values, computes the base-2 log for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of log2f, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input ±0 negative QNaN SNaN +∞ −∞ Performance: 29 cycles per 1.0. Output −∞ QNaN same QNaN same NaN converted to QNaN +∞ QNaN value for valid inputs, n = 24, longer for input values very close to Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 212 vrs4 powf: Four-valued single precision power function __m128 __vrs4_powf ( m128 x, m128 y) C Prototype: m128 vrs4 powf( m128 x, m128 y); Inputs: m128 x - the single precision input base values. m128 y - the single precision input exponent values. Outputs: m128 z - the single precision results of each x raised to the y power, returned in xmm0. Notes: vrs4 powf() computes the single precision x raised to the y power for four pairs of input arguments. This routine accepts four single precision input value pairs passed as m128 values. The result is the x raised to the y power for all four input pairs, returned as a m128 value. This is a relaxed version of powf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 0.5 ulp over the valid input range. Special case return values: Input x Input y ±0 y < 0, odd integer ±0 y < 0, not odd integer ±0 y > 0, odd integer ±0 y > 0, not odd integer −1 +∞ +1 y (incl. NaN ) x (incl. Nan) ±0 x<0 y, not integer |x|<1 −∞ |x|>1 −∞ |x|<1 +∞ |x|>1 +∞ −∞ y < 0, odd integer −∞ y < 0, not odd integer −∞ y > 0, odd integer −∞ y > 0, not odd integer +∞ y < 0, +∞ y > 0, NaN y nonzero, x<>1 NaN, Performance: 400 cycles for most valid inputs (100 cycles Output ±∞ +∞ ±0 +0 1 1 1 QNaN +∞ +0 +0 +∞ −0 +0 −∞ +∞ +0 +∞ NaN NaN per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 213 vrsa powf: Array single precision power function void vrsa_powf (int n, float *x, float *y, float *z) C Prototype: void vrsa powf(int n, float *x, float *y, float *z); Inputs: float *x - pointer to the array of single precision input x values. float *y - pointer to the array of single precision input y values. float *z pointer to the array of single precision output values. int n - the number of single precision values in both the input and output arrays. Outputs: x raised to the y value for each array pair, filled into the z array. Fortran Subroutine Interface: VRSA POWF(INTEGER*4 N, REAL*4 X(), REAL*4 Y(), REAL*4 Z()) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of real x input values. REAL Y(N) - array of real y input values. Outputs: REAL Z(N) - array of real result values. Notes: vrsa powf() computes x to the y power in single precision for each pair of elements in the x and y input arrays. This routine accepts an array of single precision input x values and an arrayi of single precision input y values, computes x^y for each input value pair, and stores the result in the array pointed to by the z input. This is a relaxed version of powf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 0.5 ulp over the valid input range. Special case return values: Input x ±0 ±0 ±0 ±0 −1 +1 x (incl. Nan) x<0 |x|<1 Input y y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer +∞ y (incl. NaN ) ±0 y, not integer −∞ Output ±∞ +∞ ±0 +0 1 1 1 QNaN +∞ Chapter 7: ACML MV: Fast Math and Fast Vector Math Library |x|>1 −∞ +0 |x|<1 +∞ +0 |x|>1 +∞ +∞ −∞ y < 0, odd integer −0 −∞ y < 0, not odd integer +0 −∞ y > 0, odd integer −∞ −∞ y > 0, not odd integer +∞ +∞ y < 0, +0 +∞ y > 0, +∞ NaN y nonzero, NaN x<>1 NaN, NaN Performance: 107 cycles per value for valid inputs, n = 24. 214 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 215 vrs4 powxf: Four-valued single precision power function with constant y __m128 __vrs4_powxf ( m128 x,float y) C Prototype: m128 vrs4 powxf( m128 x,float y); Inputs: m128 x - the single precision input base values. float y - the common single precision input exponent value. Outputs: m128 z - the single precision results of each x raised to the y power, returned in xmm0. Notes: vrs4 powxf() computes the single precision x raised to the y power for four input x arguments and a constant y input value. This routine accepts four single precision input values passed as an m128 value. The y value is passed as one single precision value. The result is the x raised to the y power for all four input values, returned as a m128 value. This is a relaxed version of powxf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 0.5 ulp over the valid input range. Special case return values: Input x ±0 ±0 ±0 ±0 −1 +1 x (incl. Nan) x<0 |x|<1 |x|>1 |x|<1 |x|>1 −∞ −∞ −∞ −∞ +∞ +∞ NaN x<>1 Input y y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer +∞ y (incl. NaN ) ±0 y, not integer −∞ −∞ +∞ +∞ y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer y < 0, y > 0, y nonzero, NaN, Output ±∞ +∞ ±0 +0 1 1 1 QNaN +∞ +0 +0 +∞ −0 +0 −∞ +∞ +0 +∞ NaN NaN Chapter 7: ACML MV: Fast Math and Fast Vector Math Library Performance: 372 cycles for most valid inputs (93 cycles per value). 216 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 217 vrsa powxf: Array single precision power function, constant y void vrsa_powf (int n, float *x, float y, float *z) C Prototype: void vrsa powxf(int n, float *x, float y, float *z); Inputs: int n - the number of single precision values in both the x input and output arrays. float *x - pointer to the array of single precision input x values. float *z - pointer to the array of single precision output values. float y - the constant single precision input y value. Outputs: x raised to the y value for each x array value, filled into the z array Fortran Subroutine Interface: VRSA POWF(INTEGER*4 N, REAL*4 X(), REAL*4 Y, REAL*4 Z()) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of real x input values. REAL Y - the constant single precision input y value. Outputs: REAL Z(N) - array of real result values. Notes: vrsa powxf() computes x to the y power in single precision for each element in the x input arrays, using a constant y. This routine accepts an array of single precision input x values and one single precision input y value, computes x^y for each x input value, and stores the result in the array pointed to by the z pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of powf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 0.5 ulp over the valid input range. Special case return values: Input x ±0 ±0 ±0 ±0 −1 Input y y < 0, odd integer y < 0, not odd integer y > 0, odd integer y > 0, not odd integer +∞ Output ±∞ +∞ ±0 +0 1 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library +1 y (incl. NaN ) 1 x (incl. Nan) ±0 1 x<0 y, not integer QNaN |x|<1 −∞ +∞ |x|>1 −∞ +0 |x|<1 +∞ +0 |x|>1 +∞ +∞ −∞ y < 0, odd integer −0 −∞ y < 0, not odd integer +0 −∞ y > 0, odd integer −∞ −∞ y > 0, not odd integer +∞ +∞ y < 0, +0 +∞ y > 0, +∞ NaN y nonzero, NaN x<>1 NaN, NaN Performance: 115 cycles per value for valid inputs, n = 24. 218 Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 219 vrd2 sin: Two-valued double precision Sine __m128d __vrd2_sin ( m128d x) C Prototype: m128d vrd2 sin( m128d x); Inputs: m128d x - the double precision input value pair. Outputs: m128d y - the double precision Sine result pair, returned in xmm0. Notes: vrd2 sin computes the Sine function of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision Sine of both values, returned as a m128d value. This is a relaxed version of sin, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 120 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (60 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 220 vrd4 sin: Four-valued double precision Sine __m128d,__m128d __vrd4_sin ( m128d x1, m128d x2) C Prototype: m128d vrd4 sin( m128d x); Note that this function uses a non-standard programming interface. The two m128d inputs, which contain four double precision values, are passed by the AMD64 C ABI in registers xmm0, and xmm1. The corresponding results are returned in xmm0 and xmm1. The use of xmm1 to return a m128d is nonstandard, and this function can not be called directly from C. It can be called directly from assembly language. It is intended for internal use by vectorizing compilers, that may be able to take advantage of the non-standard calling interface. Inputs: m128d x1 - the first double precision input value pair. m128d x2 - the second double precision input value pair. Outputs: m128d y1 - the first double precision Sine result pair, returned in xmm0. m128d y2 - second double precision Sine result pair, returned in xmm1. Notes: vrd4 sin computes the Sine function of four input arguments. This routine accepts four double precision input values passed as two m128d values. The result is the double precision Sine of the four values, returned as two m128d values. This is a relaxed version of sin, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. This routine may return slightly worse than 1 ulp for very large values between 4e5 and 5e5. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 172 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (43 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 221 vrda sin: Array double precision Sine void vrda_sin (int n, double *x, double *y) C Prototype: void vrda sin (int n, double *x, double *y) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *y - pointer to the array of output values. Outputs: Sine for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRDA SIN(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION Y(N) - array of Sines of input values. Notes: vrda sin computes the Sine function for each element of an array of input arguments. This routine accepts an array of double precision input values, computes sin(x) for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of sin, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. This routine may return slightly worse than 1 ulp for very large values between 4e5 and 5e5. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 172 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (43 cycles per value), n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 222 vrs4 sinf: Four-valued single precision Sine __m128 __vrs4_sinf ( m128 x) C Prototype: m128 vrs4 sinf( m128 x); Inputs: m128 x - the four single precision inputs. Outputs: m128 y - the four single precision Sine results, returned in xmm0. Notes: vrs4 sinf computes the Sine function of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision Sine of the four values, returned as a m128 value. This is a relaxed version of sinf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. This routine may return slightly worse than 1 ulp for very large values between 4e5 and 5e5. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 176 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (44 cycles per value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 223 vrsa sinf: Array single precision Sine void vrsa_sinf (int n, float *x, float *y) C Prototype: void vrsa sinf (int n, float *x, float *y) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *y - pointer to the array of output values. Outputs: Sine for each x value, filled into the y array. Fortran Subroutine Interface: SUBROUTINE VRSA SINF(N,X,Y) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL Y(N) - array of Sines of input values. Notes: vrsa sinf computes the Sine function for each element of an array of input arguments. This routine accepts an array of single precision input values, computes sin(x) for each input value, and stores the result in the array pointed to by the y pointer input. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of sinf, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 43 cycles per value for most valid inputs < 5e5, n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 224 vrd2 sincos: Two-valued double precision Sine and Cosine void __vrd2_sincos ( m128d x, m128d* S, m128d* C) C Prototype: void vrd2 sincos( m128d x, m128d* S, m128d* C)); Inputs: m128d x - the double precision input value pair. Outputs: (Sine of x and Cosine of x.) m128d *S - Pointer to the double precision Sine result pair. m128d *C - Pointer to the double precision Cosine result pair. Notes: vrd2 sincos computes the Sine and Cosine functions of two input arguments. This routine accepts a pair of double precision input values passed as a m128d value. The result is the double precision Sin and Cosine of both values, returned as a m128d value. This is a relaxed version of sincos, suitable for use with fastmath compiler flags or application not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 154 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (77 cycles per Sine and Cosine of a value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 225 vrda sincos: Array double precision Sine and Cosine void vrda_sincos (int n, double *x, double *ys, double *yc) C Prototype: void vrda sincos (int n, double *x, double *ys, double *yc) Inputs: int n - the number of values in both the input and output arrays. double *x - pointer to the array of input values. double *ys - pointer to the array of sin output values. double *yc - pointer to the array of cos output values. Outputs: Sine for each x value, filled into the ys array. Cosine for each x value, filled into the yc array. Fortran Subroutine Interface: SUBROUTINE VRDA SINCOS(N,X,YS,YC) Inputs: INTEGER N - the number of values in both the input and output arrays. DOUBLE PRECISION X(N) - array of double precision input values. Outputs: DOUBLE PRECISION YS(N) - array of Sines of input values. DOUBLE PRECISION YC(N) - array of Cosines of input values. Notes: vrda sincos computes the Sine and Cosine functions for each element of an array of input arguments. This routine accepts an array of double precision input values, computes sincos(x) for each input value, and stores the results in the arrays pointed to by the ys and yc pointer inputs. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of sincos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 2 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: Output same QNaN same NaN converted to QNaN QNaN QNaN Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 226 180 cycles for most valid inputs < 5e5 (43 cycles per Sin and Cos of a value), n = 24. Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 227 vrs4 sincosf: Four-valued single precision Sine and Cosine void __vrs4_sincosf ( m128 x, m128* S, m128* C) C Prototype: void vrs4 sincosf( m128 x, m128* S, m128* C)); Inputs: m128 x - the single precision input value pair. Outputs: (Sine of x and Cosine of x.) m128 *S - Pointer to the single precision Sine result pair. m128 *C - Pointer to the single precision Cosine result pair. Notes: vrs4 sincosf computes the Sine and Cosine functions of four input arguments. This routine accepts four single precision input values passed as a m128 value. The result is the single precision Sin and Cosine of all four values, returned as a m128 value. This is a relaxed version of sincosf, suitable for use with fastmath compiler flags or application not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input QNaN SNaN +∞ −∞ Performance: 220 cycles for Output same QNaN same NaN converted to QNaN QNaN QNaN most valid inputs < 5e5 (55 cycles per Sine and Cosine of a value). Chapter 7: ACML MV: Fast Math and Fast Vector Math Library 228 vrsa sincosf: Array single precision Sine and Cosine void vrsa_sincosf (int n, float *x, float *ys, float *yc) C Prototype: void vrsa sincosf (int n, float *x, float *ys, float *yc) Inputs: int n - the number of values in both the input and output arrays. float *x - pointer to the array of input values. float *ys - pointer to the array of sin output values. float *yc - pointer to the array of cos output values. Outputs: Sine for each x value, filled into the ys array. Cosine for each x value, filled into the yc array. Fortran Subroutine Interface: SUBROUTINE VRSA SINCOSF(N,X,YS,YC) Inputs: INTEGER N - the number of values in both the input and output arrays. REAL X(N) - array of single precision input values. Outputs: REAL YS(N) - array of Sines of input values. REAL YC(N) - array of Cosines of input values. Notes: vrsa sincosf computes the Sine and Cosine functions for each element of an array of input arguments. This routine accepts an array of single precision input values, computes sincos(x) for each input value, and stores the results in the arrays pointed to by the ys and yc pointer inputs. It is the responsibility of the calling program to allocate/deallocate enough storage for the output array. This is a relaxed version of sincos, suitable for use with fastmath compiler flags or applications not requiring full error handling. Denormal inputs may produce unpredictable results. Special case inputs produce C99 return values. The routine is accurate to better than 1 ulp over the valid input range. Special case return values: Input Output QNaN same QNaN SNaN same NaN converted to QNaN +∞ QNaN −∞ QNaN Performance: 53 cycles per value for most valid inputs < 5e5, n = 24. Chapter 8: References 229 8 References • [1] C.L. Lawson, R.J. Hanson, D. Kincaid, and F.T. Krogh, Basic linear algebra subprograms for Fortran usage, ACM Trans. Maths. Soft., 5 (1979), pp. 308–323. • [2] J.J. Dongarra, J. Du Croz, S. Hammarling, and R.J. Hanson, An extended set of FORTRAN basic linear algebra subroutines, ACM Trans. Math. Soft., 14 (1988), pp. 1–17. • [3] J.J. Dongarra, J. Du Croz, I.S. Duff, and S. Hammarling, A set of level 3 basic linear algebra subprograms, ACM Trans. Math. Soft., 16 (1990), pp. 1–17. • [4] David S. Dodson, Roger G. Grimes, John G. Lewis, Sparse Extensions to the FORTRAN Basic Linear Algebra Subprograms, ACM Trans. Math. Soft., 17 (1991), pp. 253–263. • [5] E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, LAPACK User’s Guide, SIAM, Philidelphia, (1999). • [6] D. E. Knuth, The Art of Computer Programming Addison-Wesley, 1997. • [7] J. Banks, Handbook on Simulation, Wiley, 1998. • [8] A. Menezes, P. van Oorschot, and S. Vanstone, Handbook of Applied Cryptography, Chapter 5, CRC Press, 1996. • [9] Chapter Introduction G05 - Random Number Generators The NAG Fortran Library Manual, Mark 21 Numerical Algorithms Group, 2005. • [10] N. M. Maclaren, The generation of multiple independent sequences of pseudorandom numbers, Appl. Statist., 1989, 38, 351-359. • [11] M. Matsumoto and T. Nishimura, Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator, ACM Transactions on Modelling and Computer Simulations, 1998. • [12] P. L’Ecuyer, Good parameter sets for combined multiple recursive random number generators, Operations Research, 1999, 47, 159-164. • [13] Programming languages - C - ISO/IEC 9899:1999 • [14] IEEE Standard for Binary Floating-Point Arithmetic (ANSI/IEEE Std 754-1985) • [15] P. L’Ecuyer and R. Simard, TestU01: A Software Library in ANSI C for Empirical Testing of Random Number Generators, Departement d’Informatique et de Recherche Operationnelle, Universite de Montreal, 2002. Software and user’s guide available at http://www.iro.umontreal.ca/~lecuyer Subject Index 230 Subject Index 2 2D FFT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3 3D FFT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 A accessing ACML (Absoft af90 under Linux) . . . . . 7 accessing ACML (Compaq Visual Fortran under 32-bit Windows). . . . . . . . . . . . . . . . . . . . . . . . . 10 accessing ACML (GNU gfortran/gcc under Linux) ......................................... 4 accessing ACML (Intel Fortran/Microsoft C under 32-bit Windows) . . . . . . . . . . . . . . . . . . . . . . . . . . 9 accessing ACML (Intel Fortran/Microsoft C under 64-bit Windows). . . . . . . . . . . . . . . . . . . . . . . . . 12 accessing ACML (Intel ifort under Linux) . . . . . . . 7 accessing ACML (Linux) . . . . . . . . . . . . . . . . . . . . . . 4 accessing ACML (NAGware f95 compiler under Linux) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 accessing ACML (other compilers under Linux) . . 8 accessing ACML (PathScale pathf90/pathcc under Linux) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 accessing ACML (PGI pgf77/pgf90/Microsoft C under 32-bit Windows) . . . . . . . . . . . . . . . . . . . . 8 accessing ACML (PGI pgf77/pgf90/pgcc or Microsoft C under 64-bit Windows) . . . . . . . 11 accessing ACML (PGI pgf77/pgf90/pgcc under Linux) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 accessing ACML (Salford ftn95 under 32-bit Windows) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 accessing ACML (Solaris) . . . . . . . . . . . . . . . . . . . . 12 accessing ACML (Sun f95/cc under Solaris) . . . . 12 accessing ACML under Windows . . . . . . . . . . . . . . . 8 accessing the base generators . . . . . . . . . . . . . . . . . 82 ACML C Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . 14 ACML FORTRAN interfaces . . . . . . . . . . . . . . . . . 14 ACML installation test . . . . . . . . . . . . . . . . . . . . . . . 17 ACML performance examples . . . . . . . . . . . . . . . . . 17 ACML version information . . . . . . . . . . . . . . . . . . . 16 ACML MV (ACML vector math functions) . . . 163 ACML MV types . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 B base base base base base base base generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . generator, basic NAG generator . . . . . . . . . . generator, blum-blum-shub . . . . . . . . . . . . . . generator, calling . . . . . . . . . . . . . . . . . . . . . . . generator, definition . . . . . . . . . . . . . . . . . . . . . generator, initialization . . . . . . . . . . . . . . . . . . generator, L’Ecuyer’s combined recursive generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . base generator, Mersenne twister . . . . . . . . . . . . . . 75 83 85 82 75 76 85 84 base generator, recommendation . . . . . . . . . . . . . . 75 base generator, user supplied . . . . . . . . . . . . . . . . . 86 base generator, Wichmann-Hill . . . . . . . . . . . . . . . 84 basic NAG base generator . . . . . . . . . . . . . . . . . . . . 83 beta distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 binomial distribution . . . . . . . . . . . . . . . . . . . . . . . . 125 binomial distribution, using reference vector . . 139 BLAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 blum-blum-shub generator . . . . . . . . . . . . . . . . . . . . 85 BRNG, definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 C C interfaces in ACML . . . . . . . . . . . . . . . . . . . . . . . . 14 calling the base generators . . . . . . . . . . . . . . . . . . . . 82 cauchy distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 99 chi-squared distribution . . . . . . . . . . . . . . . . . . . . . 101 complex FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 continuous multivariate distribution, gaussian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 continuous multivariate distribution, gaussian, using reference vector . . . . . . . . . . . . . . . . . . . 153 continuous multivariate distribution, normal . . 149 continuous multivariate distribution, normal, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 153 continuous multivariate distribution, students t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 continuous multivariate distribution, students t, using reference vector . . . . . . . . . . . . . . . . . . . 155 continuous univariate distribution, beta . . . . . . . 97 continuous univariate distribution, cauchy . . . . . 99 continuous univariate distribution, chi-squared . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 continuous univariate distribution, exponential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 continuous univariate distribution, f . . . . . . . . . . 105 continuous univariate distribution, fisher’s variance ratio distribution . . . . . . . . . . . . . . . . . . . . . . . 105 continuous univariate distribution, gamma . . . . 107 continuous univariate distribution, gaussian . . . 109 continuous univariate distribution, logistic . . . . 111 continuous univariate distribution, lognormal . . 113 continuous univariate distribution, normal . . . . 109 continuous univariate distribution, students t . . 115 continuous univariate distribution, t . . . . . . . . . . 115 continuous univariate distribution, triangular . . 117 continuous univariate distribution, uniform . . . 119 continuous univariate distribution, von mises . . 121 continuous univariate distribution, weibull . . . . 123 copying a generator . . . . . . . . . . . . . . . . . . . . . . . . . . 76 cryptologically secure, definition . . . . . . . . . . . . . . 75 cryptologically secure, generator . . . . . . . . . . . . . . 85 Subject Index D determining the best ACML version for your system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 discrete multivariate distribution, multinomial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 discrete univariate distribution, binomial . . . . . 125 discrete univariate distribution, binomial, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 139 discrete univariate distribution, geometric . . . . 127 discrete univariate distribution, geometric, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 141 discrete univariate distribution, hypergeometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 discrete univariate distribution, hypergeometric, using reference vector . . . . . . . . . . . . . . . . . . . 143 discrete univariate distribution, negative binomial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 discrete univariate distribution, negative binomial, using reference vector . . . . . . . . . . . . . . . . . . . 145 discrete univariate distribution, poisson. . . . . . . 133 discrete univariate distribution, poisson, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 147 discrete univariate distribution, uniform . . . . . . 135 distribution generator, definition . . . . . . . . . . . . . . 75 E example programs . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 exponential distribution . . . . . . . . . . . . . . . . . . . . . 103 F f distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 fast basic math functions . . . . . . . . . . . . . . . . . . . . 165 Fast Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . 24 feedback shift generator . . . . . . . . . . . . . . . . . . . . . . 84 FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 FFT efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 FFT of multiple complex sequences . . . . . . . . . . . 34 FFT of multiple Hermitian sequences. . . . . . . . . . 73 FFT of multiple real sequences . . . . . . . . . . . . . . . 69 FFT of single complex sequence. . . . . . . . . . . . . . . 27 FFT of single Hermitian sequence . . . . . . . . . . . . . 71 FFT of single real sequence . . . . . . . . . . . . . . . . . . . 67 FFT plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 fisher’s variance ratio distribution . . . . . . . . . . . . 105 FORTRAN interfaces in ACML . . . . . . . . . . . . . . . 14 G gamma distribution . . . . . . . . . . . . . . . . . . . . . . . . . 107 gaussian distribution (multivariate) . . . . . . . . . . 149 gaussian distribution (univariate) . . . . . . . . . . . . 109 gaussian distribution, multivariate, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 general information . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 generalized feedback shift generator . . . . . . . . . . . 84 generating discrete variates from a reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 geometric distribution . . . . . . . . . . . . . . . . . . . . . . . 127 231 geometric distribution, using reference vector . . 141 H Hermitian data sequences (FFT) . . . . . . . . . . . . . . 66 hypergeometric distribution . . . . . . . . . . . . . . . . . 129 hypergeometric distribution, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 I IEEE exceptions and LAPACK . . . . . . . . . . . . . . . 23 initialization of a generator . . . . . . . . . . . . . . . . . . . 76 installation test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 INTEGER*8 arguments . . . . . . . . . . . . . . . . . . . . . . 15 introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 L L’Ecuyer’s combined recursive generator . . . . . . . 85 language interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 LAPACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 LAPACK blocking factors . . . . . . . . . . . . . . . . . . . . 21 LAPACK reference sources . . . . . . . . . . . . . . . . . . . 20 libm names. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 library manual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 library version information . . . . . . . . . . . . . . . . . . . 16 linear congruential generator, basic NAG generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 linear congruential generator, Wichmann-Hill . . 84 linking with ACML . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 linking with Linux ACML . . . . . . . . . . . . . . . . . . . . . 4 linking with Solaris ACML . . . . . . . . . . . . . . . . . . . 12 linking with Windows ACML . . . . . . . . . . . . . . . . . . 8 logistic distribution . . . . . . . . . . . . . . . . . . . . . . . . . 111 lognormal distribution . . . . . . . . . . . . . . . . . . . . . . 113 M Mersenne twister . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Mersenne twister, multiple streams . . . . . . . . . . . . 91 multinomial distribution . . . . . . . . . . . . . . . . . . . . 161 multiple recursive generator, L’Ecuyer’s combined recursive generator . . . . . . . . . . . . . . . . . . . . . . 85 multiple streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 multiple streams, block splitting . . . . . . . . . . . . . . 91 multiple streams, L’Ecuyer’s combined recursive generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91, 94 multiple streams, leap frogging . . . . . . . . . . . . . . . . 94 multiple streams, Mersenne twister . . . . . . . . . . . . 91 multiple streams, NAG basic generator . . . . . 91, 94 multiple streams, skip ahead . . . . . . . . . . . . . . . . . . 91 multiple streams, using different generators . . . . 91 multiple streams, using different seeds . . . . . . . . . 91 multiple streams, Wichmann-Hill generator . . . 91, 94 multivariate distribution, gaussian . . . . . . . . . . . 149 multivariate distribution, gaussian, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 multivariate distribution, multinomial . . . . . . . . 161 Subject Index 232 multivariate distribution, normal . . . . . . . . . . . . . 149 multivariate distribution, normal, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 multivariate distribution, students t . . . . . . . . . . 151 multivariate distribution, students t, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 155 T t distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 triangular distribution . . . . . . . . . . . . . . . . . . . . . . 117 N negative binomial distribution . . . . . . . . . . . . . . . 131 negative binomial distribution, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 normal distribution (multivariate) . . . . . . . . . . . . 149 normal distribution (univariate). . . . . . . . . . . . . . 109 normal distribution, multivariate, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 P performance example programs . . . . . . . . . . . . . . . 17 period of a random number generator, definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 plan, default, FFTs . . . . . . . . . . . . . . . . . . . . . . . . . . 26 plan, generated, FFTs. . . . . . . . . . . . . . . . . . . . . . . . 26 poisson distribution . . . . . . . . . . . . . . . . . . . . . . . . . 133 poisson distribution, using reference vector . . . 147 PRNG, definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 pseudo-random number, definition . . . . . . . . . . . . 75 Q QRNG, definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 quasi-random number, definition . . . . . . . . . . . . . . 75 R random bit stream . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 real data sequences (FFT) . . . . . . . . . . . . . . . . . . . . 66 real FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 reference vector, binomial distribution . . . . . . . . 139 reference vector, gaussian (multivariate) . . . . . . 157 reference vector, generating discrete variates from . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 reference vector, geometric distribution . . . . . . . 141 reference vector, hypergeometric distribution . . 143 reference vector, negative binomial distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 reference vector, normal (multivariate) . . . . . . . 157 reference vector, poisson distribution . . . . . . . . . 147 reference vector, students t (multivariate). . . . . 159 retrieving the state of a generator . . . . . . . . . . . . . 76 S saving the state of a generator . . . . . . . . . . . . . . . . seed, definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . size of integer arguments . . . . . . . . . . . . . . . . . . . . . sparse BLAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . students t distribution . . . . . . . . . . . . . . . . . . . . . . 115 students t distribution (multivariate) . . . . . . . . . 151 students t distribution, multivariate, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 155 U uniform distribution (continuous) . . . . . . . . . . . . 119 uniform distribution (discrete) . . . . . . . . . . . . . . . 135 univariate distribution, beta . . . . . . . . . . . . . . . . . . 97 univariate distribution, binomial . . . . . . . . . . . . . 125 univariate distribution, binomial, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 univariate distribution, cauchy . . . . . . . . . . . . . . . . 99 univariate distribution, chi-squared . . . . . . . . . . . 101 univariate distribution, exponential . . . . . . . . . . 103 univariate distribution, f . . . . . . . . . . . . . . . . . . . . 105 univariate distribution, fisher’s variance ratio . . 105 univariate distribution, gamma . . . . . . . . . . 107, 109 univariate distribution, geometric . . . . . . . . . . . . 127 univariate distribution, geometric, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 univariate distribution, hypergeometric . . . . . . . 129 univariate distribution, hypergeometric, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 143 univariate distribution, logistic . . . . . . . . . . . . . . . 111 univariate distribution, lognormal . . . . . . . . . . . . 113 univariate distribution, negative binomial . . . . . 131 univariate distribution, negative binomial, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . 145 univariate distribution, normal . . . . . . . . . . . . . . . 109 univariate distribution, poisson . . . . . . . . . . . . . . 133 univariate distribution, poisson, using reference vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 univariate distribution, students t . . . . . . . . . . . . 115 univariate distribution, t . . . . . . . . . . . . . . . . . . . . 115 univariate distribution, triangular . . . . . . . . . . . . 117 univariate distribution, uniform (continuous) . . 119 univariate distribution, uniform (discrete) . . . . 135 univariate distribution, von mises . . . . . . . . . . . . 121 univariate distribution, weibull. . . . . . . . . . . . . . . 123 user supplied generators . . . . . . . . . . . . . . . . . . . . . . 86 V vector math functions . . . . . . . . . . . . . . . . . . . . . . . 183 von mises distribution . . . . . . . . . . . . . . . . . . . . . . . 121 W 76 75 15 19 weak aliases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 weibull distribution . . . . . . . . . . . . . . . . . . . . . . . . . 123 Wichmann-Hill base generator . . . . . . . . . . . . . . . . 84 Wichmann-Hill, multiple streams . . . . . . . . . . . . . 91 Routine Index 233 Routine Index __vrd2_cos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_log10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_log2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_sin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd2_sincos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_cos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_log10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_log2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrd4_sin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_cosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_expf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_log10f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_log2f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_logf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_powf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_powxf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_sincosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs4_sinf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs8_expf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs8_log10f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs8_log2f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __vrs8_logf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 188 194 200 206 219 224 184 189 195 201 207 220 186 191 203 209 197 212 215 227 222 192 204 210 198 A acmlinfo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ACMLINFO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . acmlversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ACMLVERSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 16 16 16 C CFFT1D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT1MX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT2D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT2DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CFFT3DY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSFFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSFFTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 32 37 41 45 49 54 58 63 72 74 D DRANDBETA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 DRANDBINOMIAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 DRANDBINOMIALREFERENCE . . . . . . . . . . . . . . . . . . . 139 DRANDBLUMBLUMSHUB . . . . . . . . . . . . . . . . . . . . . . . . . . 83 DRANDCAUCHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 DRANDCHISQUARED . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 DRANDDISCRETEUNIFORM. . . . . . . . . . . . . . . . . . . . . . 135 DRANDEXPONENTIAL . . . . . . . . . . . . . . . . . . . . . . . . . . 103 DRANDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 DRANDGAMMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 DRANDGAUSSIAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 DRANDGENERALDISCRETE. . . . . . . . . . . . . . . . . . . . . . 137 DRANDGEOMETRIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 DRANDGEOMETRICREFERENCE . . . . . . . . . . . . . . . . . . 141 DRANDHYPERGEOMETRIC . . . . . . . . . . . . . . . . . . . . . . . 129 DRANDHYPERGEOMETRICREFERENCE . . . . . . . . . . . . . 143 DRANDINITIALIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 DRANDINITIALIZEBBS . . . . . . . . . . . . . . . . . . . . . . . . . 81 DRANDINITIALIZEUSER . . . . . . . . . . . . . . . . . . . . . . . . 87 DRANDLEAPFROG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 DRANDLOGISTIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 DRANDLOGNORMAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 DRANDMULTINOMIAL . . . . . . . . . . . . . . . . . . . . . . . . . . 161 DRANDMULTINORMAL . . . . . . . . . . . . . . . . . . . . . . . . . . 149 DRANDMULTINORMALR . . . . . . . . . . . . . . . . . . . . . . . . . 153 DRANDMULTINORMALREFERENCE . . . . . . . . . . . . . . . . 157 DRANDMULTISTUDENTSREFERENCE . . . . . . . . . . . . . . 159 DRANDMULTISTUDENTST . . . . . . . . . . . . . . . . . . . . . . . 151 DRANDMULTISTUDENTSTR. . . . . . . . . . . . . . . . . . . . . . 155 DRANDNEGATIVEBINOMIAL . . . . . . . . . . . . . . . . . . . . 131 DRANDNEGATIVEBINOMIALREFERENCE . . . . . . . . . . . 145 DRANDPOISSON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 DRANDPOISSONREFERENCE . . . . . . . . . . . . . . . . . . . . 147 DRANDSKIPAHEAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 DRANDSTUDENTST . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 DRANDTRIANGULAR . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 DRANDUNIFORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 DRANDVONMISES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 DRANDWEIBULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 DZFFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 DZFFTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 F fastcos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastcosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastexp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastexpf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlog10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlog10f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlog2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlog2f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastlogf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastpow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastpowf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsincos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsincosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fastsinf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 166 167 168 169 171 172 173 174 170 175 177 179 181 182 180 Routine Index I ILAENVSET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 S SCFFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 SCFFTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 SRANDBINOMIALREFERENCE . . . . . . . . . . . . . . . . . . . 139 SRANDCHISQUARED . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 SRANDGEOMETRICREFERENCE . . . . . . . . . . . . . . . . . . 141 SRANDBETA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 SRANDBINOMIAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 SRANDBLUMBLUMSHUB . . . . . . . . . . . . . . . . . . . . . . . . . . 83 SRANDCAUCHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 SRANDDISCRETEUNIFORM. . . . . . . . . . . . . . . . . . . . . . 135 SRANDEXPONENTIAL . . . . . . . . . . . . . . . . . . . . . . . . . . 103 SRANDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 SRANDGAMMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 SRANDGAUSSIAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 SRANDGENERALDISCRETE. . . . . . . . . . . . . . . . . . . . . . 137 SRANDGEOMETRIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 SRANDHYPERGEOMETRIC . . . . . . . . . . . . . . . . . . . . . . . 129 SRANDHYPERGEOMETRICREFERENCE . . . . . . . . . . . . . 143 SRANDINITIALIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 SRANDINITIALIZEBBS . . . . . . . . . . . . . . . . . . . . . . . . . 81 SRANDINITIALIZEUSER . . . . . . . . . . . . . . . . . . . . . . . . 87 SRANDLEAPFROG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 SRANDLOGISTIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 SRANDLOGNORMAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 SRANDMULTINOMIAL . . . . . . . . . . . . . . . . . . . . . . . . . . 161 SRANDMULTINORMAL . . . . . . . . . . . . . . . . . . . . . . . . . . 149 SRANDMULTINORMALR . . . . . . . . . . . . . . . . . . . . . . . . . 153 SRANDMULTINORMALREFERENCE . . . . . . . . . . . . . . . . 157 SRANDMULTISTUDENTST . . . . . . . . . . . . . . . . . . . . . . . 151 SRANDMULTISTUDENTSTR. . . . . . . . . . . . . . . . . . . . . . 155 SRANDMULTISTUDENTSTREFERENCE . . . . . . . . . . . . . 159 SRANDNEGATIVEBINOMIAL . . . . . . . . . . . . . . . . . . . . 131 SRANDNEGATIVEBINOMIALREFERENCE . . . . . . . . . . . 145 SRANDPOISSON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 SRANDPOISSONREFERENCE . . . . . . . . . . . . . . . . . . . . 147 SRANDSKIPAHEAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 234 SRANDSTUDENTST . . . . . . . . . . . . . . . . . . . . . . . . . . . . SRANDTRIANGULAR . . . . . . . . . . . . . . . . . . . . . . . . . . . SRANDUNIFORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SRANDVONMISES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SRANDWEIBULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 117 119 121 123 U UGEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 UINI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 V vrda_cos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_log10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_log2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_sin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrda_sincos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_cosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_expf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_log10f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_log2f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_logf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_powf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213, vrsa_sincosf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vrsa_sinf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 190 196 202 208 221 225 187 193 205 211 199 217 228 223 Z ZDFFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZDFFTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT1MX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT2D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT2DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT3DX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ZFFT3DY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 73 28 30 35 39 44 46 53 56 60

- Similar pages
- Si4438-C High-Performance Low-Current Transceiver
- TDC-GP30 CPU Memory-Firmware
- MITEL PDSP16116MCGGDR
- ETC ADC-44D
- PIC Microcontroller Compiled Tips 'n Tricks Guide
- MITEL PDSP16510AMAAC1R
- STMICROELECTRONICS STPM01FTR
- Media:Sprueb8b.pdf
- ETC RH1020
- View detail for ATSHA204A
- QPI-21 - Vicor
- INFINEON SBH52414Z
- INFINEON SBH92314N-FSAN
- INFINEON SBH52454Z-FSAN
- INFINEON SBH92344N-FSAN
- 7014