我想知道为什么numpy的linalg.solve没有使用所有可用的线程来做它的演算。
我用它来解决一个多维系统，在某种程度上，它应该解决找到一个向量与27个条目n.N.N次。所以，因为它必须做同样类型的计算很多次，我以为它会利用所有可用的线程，但它不是实际发生的事情。它只使用16个线程，即使我改变n，我已经测试了n = 300和400。我还尝试从threadpoolctl手动指定线程，它只会减少运行线程的数量，但没有看到性能上的差异（不知道为什么）。
测试代码为

import numpy as np
from numpy import *
from timeit import default_timer as timer
import math;

import os, psutil
process = psutil.Process(os.getpid())

###### Main time loop ##########################################################

n = 400
N = 300

s = timer()

k_star = np.random.rand(27*n*N*N).reshape((27,n,N,N))
T = np.random.rand(27*27*n*N*N).reshape((27,27,n,N,N))

t1 = timer() - s

print("time to allocate k_star and T = " + str(t1))


for time in range(0,1001):

    s = timer()

    fout = np.transpose(linalg.solve(np.transpose(T),np.transpose(k_star)))

    t = timer() - s

    print("time to calculate fout solving the system = " + str(t))

    print(time)
    
    if time%1000 == 0:
        memory = process.memory_info().rss/1000000000
        print("RAM Memory occupied = " + str(round(memory,2)) + " Gb")

可能对答案有用，np.__config__.show()的输出是

openblas64__info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)]
    runtime_library_dirs = ['/usr/local/lib']
blas_ilp64_opt_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)]
    runtime_library_dirs = ['/usr/local/lib']
openblas64__lapack_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)]
    runtime_library_dirs = ['/usr/local/lib']
lapack_ilp64_opt_info:
    libraries = ['openblas64_', 'openblas64_']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)]
    runtime_library_dirs = ['/usr/local/lib']
Supported SIMD extensions in this NumPy install:
    baseline = SSE,SSE2,SSE3
    found = SSSE3,SSE41,POPCNT,SSE42,AVX,F16C,FMA3,AVX2,AVX512F,AVX512CD,AVX512_SKX,AVX512_CLX
    not found = AVX512_KNL,AVX512_KNM,AVX512_CNL,AVX512_ICL

我将感谢任何帮助，以了解正在发生的事情，以及如何通过使用所有可用的线程来提高运行代码的性能。