python配置核_python – numpy在多核硬件上

对于内部和外部矢量产品,矢量矩阵乘法等方面的内容,在使用多个核心(在Intel硬件上),可以使用多少核心？

如果有必要,我很乐意重建numpy,但在这一点上,我正在考虑如何加快速度,而不改变我的代码.

为了参考,我的show_config()如下,我从来没有观察到使用多个核心的numpy：

atlas_threads_info:

libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']

library_dirs = ['/usr/local/atlas-3.9.16/lib']

language = f77

include_dirs = ['/usr/local/atlas-3.9.16/include']

blas_opt_info:

libraries = ['ptf77blas', 'ptcblas', 'atlas']

library_dirs = ['/usr/local/atlas-3.9.16/lib']

define_macros = [('ATLAS_INFO', '"\"3.9.16\""')]

language = c

include_dirs = ['/usr/local/atlas-3.9.16/include']

atlas_blas_threads_info:

libraries = ['ptf77blas', 'ptcblas', 'atlas']

library_dirs = ['/usr/local/atlas-3.9.16/lib']

language = c

include_dirs = ['/usr/local/atlas-3.9.16/include']

lapack_opt_info:

libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']

library_dirs = ['/usr/local/atlas-3.9.16/lib']

define_macros = [('ATLAS_INFO', '"\"3.9.16\""')]

language = f77

include_dirs = ['/usr/local/atlas-3.9.16/include']

lapack_mkl_info:

NOT AVAILABLE

blas_mkl_info:

NOT AVAILABLE

mkl_info:

NOT AVAILABLE