I noticed that when I run a numpy function like numpy.dot
, 4 cores of my machine will get busy doing the work. This means that, somehow, numpy automatically understands that it can parallelize some computations. However, I actually have 8 cores on my machine, so I would like to tell numpy to use all of them.
Do you know how to do that?
I've followed the instrutions in Limit number of threads in numpy, but they didn't work.
I report the output of numpy.show_config()
below:
lapack_opt_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/home/anfri/anaconda2/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/home/anfri/anaconda2/include']
blas_opt_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/home/anfri/anaconda2/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/home/anfri/anaconda2/include']
lapack_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/home/anfri/anaconda2/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/home/anfri/anaconda2/include']
blas_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/home/anfri/anaconda2/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/home/anfri/anaconda2/include']