Accelerated Numpy on Pi 3 B+

Question

I'm looking to do some work with NumPy on my Pi 3 B+, and I'm not sure what the deal is with optimization, blas, etc.

I installed libatlas using:

sudo apt-get install libatlas-base-dev

Following which I installed numpy:

pip3 install numpy

Note that my default python is python 3.5. When I run the python code:

import numpy as np
print (np.__config__.show())

I get the following output

atlas_info:
    include_dirs = ['/usr/include/atlas']
    language = f77
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas', 'f77blas', 'cblas']
    define_macros = [('ATLAS_INFO', '"\\"3.10.3\\""')]
    library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base']
atlas_3_10_info:
  NOT AVAILABLE
atlas_3_10_threads_info:
  NOT AVAILABLE
openblas_clapack_info:
  NOT AVAILABLE
atlas_3_10_blas_threads_info:
  NOT AVAILABLE
lapack_mkl_info:
  NOT AVAILABLE
atlas_blas_info:
    library_dirs = ['/usr/lib/atlas-base']
    include_dirs = ['/usr/include/atlas']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('ATLAS_INFO', '"\\"3.10.3\\""')]
    libraries = ['f77blas', 'cblas', 'atlas', 'f77blas', 'cblas']
accelerate_info:
  NOT AVAILABLE
atlas_blas_threads_info:
  NOT AVAILABLE
atlas_3_10_blas_info:
  NOT AVAILABLE
atlas_threads_info:
  NOT AVAILABLE
blis_info:
  NOT AVAILABLE
openblas_info:
  NOT AVAILABLE
blas_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
  NOT AVAILABLE
blas_opt_info:
    library_dirs = ['/usr/lib/atlas-base']
    include_dirs = ['/usr/include/atlas']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('ATLAS_INFO', '"\\"3.10.3\\""')]
    libraries = ['f77blas', 'cblas', 'atlas', 'f77blas', 'cblas']
lapack_opt_info:
    include_dirs = ['/usr/include/atlas']
    language = f77
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas', 'f77blas', 'cblas']
    define_macros = [('ATLAS_INFO', '"\\"3.10.3\\""')]
    library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base']
None

This looks to me like numpy is optimized with blas, and therefore for multi-core operation, however when I run a matrix multiplication program and run

top

The python process tops out at 100% cpu instead of 400% like I see on my 4-core laptop...