为什么复制= 16 GB Numpy数组会将其所有元素都设置为0？

在我的Anaconda Python发行版上，复制恰好为16 GB或更大的Numpy数组(不管数据类型如何)会将副本的所有元素设置为0：

>>> np.arange(2 ** 31 - 1).copy()  # works fine
array([         0,          1,          2, ..., 2147483644, 2147483645,
       2147483646])
>>> np.arange(2 ** 31).copy()  # wait, what?!
array([0, 0, 0, ..., 0, 0, 0])
>>> np.arange(2 ** 32 - 1, dtype=np.float32).copy()
array([  0.00000000e+00,   1.00000000e+00,   2.00000000e+00, ...,
         4.29496730e+09,   4.29496730e+09,   4.29496730e+09], dtype=float32)
>>> np.arange(2 ** 32, dtype=np.float32).copy()
array([ 0.,  0.,  0., ...,  0.,  0.,  0.], dtype=float32)

下面是这个发行版的np.__config__.show()：

blas_opt_info:
    library_dirs = ['/users/username/.anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/users/username/.anaconda3/include']
    libraries = ['mkl_rt', 'pthread']
lapack_opt_info:
    library_dirs = ['/users/username/.anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/users/username/.anaconda3/include']
    libraries = ['mkl_rt', 'pthread']
mkl_info:
    library_dirs = ['/users/username/.anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/users/username/.anaconda3/include']
    libraries = ['mkl_rt', 'pthread']
openblas_lapack_info:
  NOT AVAILABLE
lapack_mkl_info:
    library_dirs = ['/users/username/.anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/users/username/.anaconda3/include']
    libraries = ['mkl_rt', 'pthread']
blas_mkl_info:
    library_dirs = ['/users/username/.anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/users/username/.anaconda3/include']
    libraries = ['mkl_rt', 'pthread']

为了便于比较，下面是我的system Python发行版的np.__config__.show()，它没有这个问题：

blas_opt_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas', 'openblas']
    language = c
    library_dirs = ['/usr/local/lib']
openblas_lapack_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas', 'openblas']
    language = c
    library_dirs = ['/usr/local/lib']
openblas_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas', 'openblas']
    language = c
    library_dirs = ['/usr/local/lib']
lapack_opt_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas', 'openblas']
    language = c
    library_dirs = ['/usr/local/lib']
blas_mkl_info:
  NOT AVAILABLE

我想知道MKL加速是不是有问题。我已经在Python 2和Python 3上重现了这个bug。

转载请注明出处:http://www.jubohx.com/article/20230523/1603488.html