Numpy/Scipy with Intel® MKL-白红宇

Numpy/Scipy with Intel® MKL

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发布时间：2019-05-26

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NumPy/SciPy Application Note

Step 1 - Overview

This guide is intended to help current NumPy/SciPy users to take advantage of Intel® Math Kernel Library (Intel® MKL).

NumPy automatically maps operations on vectors and matrices to the BLAS and LAPACK functions wherever possible. Since Intel® MKL supports these de-facto interfaces, NumPy can benefit from Intel MKL optimizations through simple modifications to the NumPy scripts.

is the fundamental package required for scientific computing with Python. It consists of:

a powerful N-dimensional array object

sophisticated (broadcasting) functions

tools for integrating C/C++ and Fortran code

useful linear algebra, Fourier transform, and random number capabilities.

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.

For more information on NumPy, please visit http://NumPy.scipy.org/

SciPy include modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization for python users. Please refer for more details on SciPy.

Version Information

This application note was created to help NumPy/SciPy users to make use of the latest versions of Intel MKL on Linux platforms.

The instructions given in this articles apply to Intel MKL 10.3 and above and Intel Compiler 11.0 and above.

Step 2 - Downloading NumPy and SciPy Source Code

The NumPy source code can be downloaded from:

Prerequisites

Intel MKL can be obtained from the following options:

Download a FREE evaluation version of the Intel MKL product.

Download the FREE non-commercial* version of the Intel MKL product.

All of these can be obtained at: .

Intel® MKL is also bundled with the following products

Step 3 - Configuration

Use the following commands to extract the NumPy tar files from the downloaded NumPy-x.x.x.tar.gz.

[shell]$gunzip numpy-x.x.x.tar.gz $tar -xvf numpy-x.x.x.tar [/shell]

The above will create a directory named numpy-x.x.x

And to extract SciPy, use the below commands

[shell]$gunzip scipy-x.x.x.tar.gz $tar -xvf scipy-x.x.x.tar.gz [/shell]

The scipy-x.x.x directory will be created with extracted files.

Make sure that C++ and FORTRAN compilers are installed and they are in PATH. Also set LD_LIBRARY_PATH to your compiler (C++ and FORTRAN), and MKL libraries.

Step 4 - Building and Installing NumPy

Change directory to numpy-x.x.x

Create a site.cfg from the existing one

Edit site.cfg as follows:

Add the following lines to site.cfg in your top level NumPy directory to use Intel® MKL, if you are building on Intel 64 platform:

[mkl]library_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64include_dirs = /opt/intel/composer_xe_2011.sp1.6.233/mkl/includemkl_libs = mkl_rtlapack_libs =

If you are building NumPy for 32 bit, please add as the following

[mkl] library_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/ia32include_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/includemkl_libs = mkl_rtlapack_libs =

Modify cc_exe in numpy/distutils/intelccompiler.py to be something like:

self.cc_exe = 'icc -O3 -g -fPIC -fp-model strict -fomit-frame-pointer -openmp -xhost'

Here we use, -O3, optimizations for speed and enables more aggressive loop transformations such as Fusion, Block-Unroll-and-Jam, and collapsing IF statements, -openmp for OpenMP threading and -xhost option tells the compiler to generate instructions for the highest instruction set available on the compilation host processor. If you are using the ILP64 interface, please add -DMKL_ILP64 compiler flag.

Run icc --help for more information on processor-specific options, and refer Intel Compiler documentation for more details on the various compiler flags.

Modify the the Fortran compiler configuration in numpy-x.x.x/numpy/distutil/fcompiler/intel.py to use the following compiler options for the Intel Fortran Compiler:

For ia32 and Intel64

ifort -xhost -openmp -fp-model strict -fPIC

If you are using ILP64 interface of Intel MKL, please add -i8 flag above. Please download the modified for your reference, which can be replaced to use the above mentioned compiler options.

Compile and install NumPy with the Intel compiler: (on 64-bit platforms replace "intel" with "intelem")

python setup.py config --compiler=intel build_clib --compiler=intel build_ext --compiler=intel install

Build and Install SciPy

Compile and install SciPy with the Intel Compilers: (On 64-bit platforms replace "intel" with "intelem")

$python setup.py config --compiler=intel --fcompiler=intel build_clib --compiler=intel --fcompiler=intel build_ext --compiler=intel --fcompiler=intel install

Setup Library path for Intel MKL and Intel Compilers

If you build NumPY/SciPy for Intel64 bit platforms:

[shell]$export LD_LIBRARY_PATH=/opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64:/opt/intel/composer_xe_2011_sp1.6.233/compiler/lib/intel64:$LD_LIBRARY_PATH [/shell]

If you build NumPY for ia32 bit platforms:

[shell]$export LD_LIBRARY_PATH=/opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/ia32:/opt/intel/composer_xe_2011_sp1.6.233/compiler/lib/ia32:$LD_LIBRARY_PATH [/shell]

It is possible that LD_LIBRARY_PATH causes a problem, if you have installed Intel MKL and Intel Composer XE in other directories than the standard ones. The only solution I've found that always works is to build Python, NumPy and SciPy inside an environment where you've set the LD_RUN_PATH variable, e.g:

$export LD_RUN_PATH=~/opt/lib:~/intel/composer_xe_2011_sp1.6.233/compiler/lib:~/intel/composer_xe_2011_sp1.6.233/mkl/lib/ia32

Note:We recommend users to use arrays with 'C' ordering style which is row-major, which is default than Fortran Style which is column-major, and this is because NumPy uses CBLAS and also to get better performance.

Appendex A: Example:

Please see below an example Python script for matrix multiplication that you can use Numply installed with Intel MKL which has been provided for illustration purpose.

import numpy as np  import time    N = 6000  M = 10000    k_list = [64, 80, 96, 104, 112, 120, 128, 144, 160, 176, 192, 200, 208, 224, 240, 256, 384]    def get_gflops(M, N, K):      return M*N*(2.0*K-1.0) / 1000**3    np.show_config()    for K in k_list:      a = np.array(np.random.random((M, N)), dtype=np.double, order='C', copy=False)      b = np.array(np.random.random((N, K)), dtype=np.double, order='C', copy=False)      A = np.matrix(a, dtype=np.double, copy=False)      B = np.matrix(b, dtype=np.double, copy=False)        C = A*B        start = time.time()        C = A*B      C = A*B      C = A*B      C = A*B      C = A*B        end = time.time()        tm = (end-start) / 5.0        print "{0:4}, {1:9.7}, {2:9.7}".format(K, tm, get_gflops(M, N, K) / tm)

Appendix B: Performance Comparison