numpy向量运算中核心使用的上限

Question

我喜欢numpy因为多核会自动用于矢量操作。

但是，我最近注意到虽然我的机器有8个核心，但只使用了4个核心。

为什么numpy不使用机器中的所有核心？是否有可能让numpy在numpy的向量操作上使用更多内核？

我正在使用Mac OSX 10.8。

更新

np.show_config()如下所示：

lapack_opt_info:
    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']
    library_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/include']
blas_opt_info:
    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']
    library_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/include']
openblas_lapack_info:
  NOT AVAILABLE
lapack_mkl_info:
    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']
    library_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/include']
blas_mkl_info:
    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']
    library_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/include']
mkl_info:
    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread']
    library_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/Users/john/.pyenv/versions/anaconda-2.0.1/include']

Answer 1

理想情况下，

MKL已经为您的系统使用了优化的线程数。您可以查看以下内容：

python -c "import ctypes; mkl_rt = ctypes.CDLL('mkl_rt.dll'); print mkl_rt.mkl_get_max_threads()"

如果您需要更改它，您可能想要更改MKL_NUM_THREADS环境变量：

python -c "import ctypes; mkl_rt = ctypes.CDLL('mkl_rt.dll'); print mkl_rt.mkl_get_max_threads()"

有关MKL多线程检查的更多信息this和API。

我引用了英特尔的Anton Malakhov。您可能想要检查该问题（配置MKL NumPy可用的处理器数量），因为它与您的非常相似。