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Currently, they are 6 | - Breeze, which can use any BLAS library with CBLAS interface by means of underlying Netlib-java 7 | - OpenBLAS, open source CPU optimized library 8 | - NVBLAS, proprietary Fortran-BLAS wrapper for few functions from GPU optimized library CUBLAS 9 | - Intel MKL, proprietary CPU optimized library 10 | - f2jblas, reference Java BLAS implementation 11 | - BIDMat, based on Intel MKL and GPU CUDA 12 | - Own wrapper and optimizations for GPU CUBLAS 13 | - Own wrapper and optimizations for Intel MKL 14 | 15 | ## Sources 16 | Sources contain Scala code snippets that are used to run benchmarks 17 | 18 | ## Results 19 | Spreadsheets with results, each tab is a different hardware configuration: 20 | https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit#gid=0 21 | 22 | ## Glossary 23 | - BLAS - Basic Linear Algebra Subprograms, general term for linear algebra in software, might be misleading becuase it does not mean any specific interface 24 | - Fortran BLAS - Reference Fortran interface for BLAS 25 | - CBLAS - reference C interface for BLAS 26 | - CUBLAS - proprietary (non-standard) interface for BLAS 27 | - NVBLAS - proprietary reference Fortran-BLAS wrapper for few functions from GPU optimized library CUBLAS 28 | - Netlib - repository of software for scientific computing 29 | 30 | ## Relevant links 31 | - Netlib, Reference BLAS and CBLAS http://www.netlib.org/blas/ 32 | - Netlib-java https://github.com/fommil/netlib-java 33 | - Breeze https://github.com/scalanlp/breeze 34 | - BIDMat https://github.com/BIDData/BIDMat/ 35 | - OpenBLAS https://github.com/xianyi/OpenBLAS 36 | - CUDA http://www.nvidia.com/object/cuda_home_new.html 37 | - NVBLAS http://docs.nvidia.com/cuda/nvblas 38 | -------------------------------------------------------------------------------- /src/bidmat.scala: -------------------------------------------------------------------------------- 1 | // With some step, square matrices and multiple runs 2 | val minDim = 1000 3 | val maxDim = 64000 4 | val step = 1000 5 | val repetitions = 10 6 | val range = for (b <- minDim to maxDim by step) yield b 7 | //val range = for (b <- 6 to 14) yield { math.pow(2, b).toInt} 8 | for (i <- 0 until range.length) { 9 | var result = new Array[Double](repetitions) 10 | val dim = range(i) 11 | for (j <- 0 until repetitions) { 12 | val a = grand(dim, dim); 13 | val b = grand(dim, dim); 14 | System.gc(); 15 | val t = System.nanoTime(); 16 | val c = a * b; 17 | val totalTime = System.nanoTime() - t; 18 | result(j) = totalTime; 19 | a.free 20 | b.free 21 | c.free 22 | } 23 | val someValue = result.last 24 | scala.util.Sorting.quickSort(result) 25 | val median = (result(repetitions / 2) + result(repetitions / 2 - 1)) / 2 26 | val avg = result.sum / result.length 27 | val gigaFlops = 2.0 / median * dim * dim * dim 28 | println(dim + "\t" + median / 1e9 + "\t" + avg / 1e9 + "\t" + someValue / 1e9 + "\t" + gigaFlops) 29 | } 30 | -------------------------------------------------------------------------------- /src/breeze.scala: -------------------------------------------------------------------------------- 1 | // With some step, square matrices and multiple runs 2 | val minDim = 1000 3 | val maxDim = 46000 4 | val step = 1000 5 | val repetitions = 10 6 | val range = for (b <- minDim to maxDim by step) yield b 7 | //val range = for (b <- 6 to 14) yield { math.pow(2, b).toInt} 8 | for (i <- 0 until range.length) { 9 | var result = new Array[Double](repetitions) 10 | val dim = range(i) 11 | for (j <- 0 until repetitions) { 12 | val a = breeze.linalg.DenseMatrix.rand[Double](dim, dim).mapValues(_.toFloat); 13 | val b = breeze.linalg.DenseMatrix.rand[Double](dim, dim).mapValues(_.toFloat); 14 | System.gc(); 15 | val t = System.nanoTime(); 16 | val c = a * b; 17 | val totalTime = System.nanoTime() - t; 18 | result(j) = totalTime; 19 | } 20 | val someValue = result.last 21 | scala.util.Sorting.quickSort(result) 22 | val median = (result(repetitions / 2) + result(repetitions / 2 - 1)) / 2 23 | val avg = result.sum / result.length 24 | val gigaFlops = 2.0 / median * dim * dim * dim 25 | println(dim + "\t" + median / 1e9 + "\t" + avg / 1e9 + "\t" + someValue / 1e9 + "\t" + gigaFlops) 26 | } 27 | --------------------------------------------------------------------------------