找出Numpy是否/使用哪个BLAS库 [英] Find out if/which BLAS library is used by Numpy

问题描述

我在不同的环境(MacOS，Ubuntu，RedHat)中使用numpy和scipy. 通常，我会使用可用的软件包管理器来安装numpy(例如，mac端口，apt，yum).

I use numpy and scipy in different environments (MacOS, Ubuntu, RedHat). Usually I install numpy by using the package manager that is available (e.g., mac ports, apt, yum).

但是，如果不手动编译Numpy，如何确定它使用BLAS库?使用mac端口，将ATLAS作为依赖项安装.但是，我不确定它是否真的使用过.当我执行简单的基准测试时，numpy.dot()函数需要大约.时间是使用Eigen C ++库计算的点积的2倍.我不确定这是否是合理的结果.

However, if you don't compile Numpy manually, how can you be sure that it uses a BLAS library? Using mac ports, ATLAS is installed as a dependency. However, I am not sure if it is really used. When I perform a simple benchmark, the numpy.dot() function requires approx. 2 times the time than a dot product that is computed using the Eigen C++ library. I am not sure if this is a reasonable result..

最诚挚的问候， 载脂蛋白

Best regards, Apo

推荐答案

numpy.show_config()并不总是提供可靠的信息.例如，如果我在Ubuntu 14.04上为apt-get install python-numpy，则np.show_config()的输出如下所示:

numpy.show_config() doesn't always give reliable information. For example, if I apt-get install python-numpy on Ubuntu 14.04, the output of np.show_config() looks like this:

blas_info:
    libraries = ['blas']
    library_dirs = ['/usr/lib']
    language = f77
lapack_info:
    libraries = ['lapack']
    library_dirs = ['/usr/lib']
    language = f77
atlas_threads_info:
  NOT AVAILABLE
blas_opt_info:
    libraries = ['blas']
    library_dirs = ['/usr/lib']
    language = f77
    define_macros = [('NO_ATLAS_INFO', 1)]
atlas_blas_threads_info:
  NOT AVAILABLE
openblas_info:
  NOT AVAILABLE
lapack_opt_info:
    libraries = ['lapack', 'blas']
    library_dirs = ['/usr/lib']
    language = f77
    define_macros = [('NO_ATLAS_INFO', 1)]
...

看起来numpy正在使用标准CBLAS库.但是，我知道numpy使用的是OpenBLAS，它是通过libopenblas-dev软件包安装的.

It looks as though numpy is using the standard CBLAS library. However, I know for a fact that numpy is using OpenBLAS, which I installed via the libopenblas-dev package.

检查* nix的最明确方法是使用 ldd 来找出共享的内容库在运行时针对numpy链接(我没有Mac，但我认为您可以使用otool -L代替ldd).

The most definitive way to check on *nix is to use ldd to find out which shared libraries numpy links against at runtime (I don't own a Mac, but I think you can use otool -L in place of ldd).

• 对于numpy低于v1.10的版本:

~$ ldd /<path_to_site-packages>/numpy/core/_dotblas.so

如果_dotblas.so不存在，则可能意味着numpy最初编译时numpy无法检测到任何BLAS库，在这种情况下，它根本不构建任何依赖于BLAS的组件.

If _dotblas.so doesn't exist, this probably means that numpy failed to detect any BLAS libraries when it was originally compiled, in which case it simply doesn't build any of the BLAS-dependent components.

对于numpy v1.10及更高版本:

_dotblas.so已被删除，但是您可以检查依赖关系代替multiarray.so:

~$ ldd /<path_to_site-packages>/numpy/core/multiarray.so

查看我通过apt-get安装的numpy版本:

Looking at the version of numpy I installed via apt-get:

~$ ldd /usr/lib/python2.7/dist-packages/numpy/core/_dotblas.so 
    linux-vdso.so.1 =>  (0x00007fff12db8000)
    libblas.so.3 => /usr/lib/libblas.so.3 (0x00007fce7b028000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fce7ac60000)
    libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fce7a958000)
    libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007fce7a738000)
    /lib64/ld-linux-x86-64.so.2 (0x00007fce7ca40000)

/usr/lib/libblas.so.3实际上是符号链接链的开始.如果我使用readlink -e跟随他们达到最终目标，我会发现它们指向我的OpenBLAS共享库:

/usr/lib/libblas.so.3 is actually the start of a chain of symlinks. If I follow them to their ultimate target using readlink -e, I see that they point to my OpenBLAS shared library:

~$ readlink -e /usr/lib/libblas.so.3
/usr/lib/openblas-base/libblas.so.3

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