numpy的QR出现比scipy快

出于某种原因，numpy的QR分解总是比scipy更快，这似乎很奇怪，因为scipy应该包含所有numpy和更高级的功能。 任何想法为什么？

import numpy.linalg as nla
import scipy.linalg as la
A = np.random.randn(4000,4000)

%timeit -n 3 -r 3 Q,R = nla.qr(A)
%timeit -n 3 -r 3 Q,R = la.qr(A)
%timeit -n 3 -r 3 Q,R = nla.qr(A)
%timeit -n 3 -r 3 Q,R = la.qr(A)

3个循环，最好为3：每循环1.17秒

3个回路，最好3个：每回路1.21秒

3个循环，最好是3个：每个循环1.05秒

3个回路，最好3个：每回路1.21秒

这种差异在mode =“raw”时更明显。

%timeit -n 3 -r 3 Q = nla.qr(A, mode='raw')
%timeit -n 3 -r 3 Q = la.qr(A, mode='raw')
%timeit -n 3 -r 3 Q = nla.qr(A, mode='raw')
%timeit -n 3 -r 3 Q = la.qr(A, mode='raw')

3个循环，最好为3：每个循环440毫秒

3个回路，每个回路3：612 ms

3个回路，最好3个：每个回路436 ms

3个回路，每个回路3：758 ms

我正在使用英特尔MKL，而且我有最新的开发版本

numpy版本：1.11.0.dev0 + 90a1a9f

scipy版本：0.17.0.dev0 + 8003fab

NumPy config:
blas_opt_info:
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    libraries = ['mkl_rt', 'pthread']
mkl_info:
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    libraries = ['mkl_rt', 'pthread']
lapack_opt_info:
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    libraries = ['mkl_rt', 'pthread']
openblas_lapack_info:
  NOT AVAILABLE
lapack_mkl_info:
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    libraries = ['mkl_rt', 'pthread']
blas_mkl_info:
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    libraries = ['mkl_rt', 'pthread']

SciPy config:
blas_opt_info:
    libraries = ['mkl_rt', 'pthread']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
mkl_info:
    libraries = ['mkl_rt', 'pthread']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
blas_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
openblas_lapack_info:
  NOT AVAILABLE
lapack_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']
lapack_opt_info:
    libraries = ['mkl_rt', 'pthread']
    include_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/include']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    library_dirs = ['/opt/intel/compilers_and_libraries_2016/linux/mkl/lib/intel64']

链接地址: http://www.djcxy.com/p/85701.html

上一篇: numpy's QR appears faster than scipy's

下一篇: Comparing runtime of standard list comprehension vs. NumPy