我给我的小宝贝做了个简单的速度测试：

import numpy as np

A = np.random.rand(1000, 1000)
B = np.random.rand(1000, 1000)

%timeit A.dot(B)

结果是：

30.3 ms ± 829 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

与其他人通常看到的结果相比（平均不到10毫秒），这个结果似乎异常缓慢。我想知道这种行为的原因可能是什么。
我的系统是MacOS Big Sur，基于M1芯片，Python版本是3.8.13，numpy版本是1.22.4，numpy是通过

pip install "numpy==1.22.4"

np.show_config()的输出为：

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
    not found = AVX,F16C,FMA3,AVX2,AVX512F,AVX512CD,AVX512_KNL,AVX512_SKX,AVX512_CLX,AVX512_CNL,AVX512_ICL

编辑：
我用这个代码片段做了另一个测试（来自1）：

import time
import numpy as np
np.random.seed(42)
a = np.random.uniform(size=(300, 300))
runtimes = 10

timecosts = []
for _ in range(runtimes):
    s_time = time.time()
    for i in range(100):
        a += 1
        np.linalg.svd(a)
    timecosts.append(time.time() - s_time)

print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s')

我的测试结果是：

mean of 10 runs: 6.17438s

而网站1上的参考结果是：（芯片为M1 Max）

+-----------------------------------+-----------------------+--------------------+
|   Python installed by (run on)→   | Miniforge (native M1) | Anaconda (Rosseta) |
+----------------------+------------+------------+----------+----------+---------+
| Numpy installed by ↓ | Run from → |  Terminal  |  PyCharm | Terminal | PyCharm |
+----------------------+------------+------------+----------+----------+---------+
|          Apple Tensorflow         |   4.19151  |  4.86248 |     /    |    /    |
+-----------------------------------+------------+----------+----------+---------+
|        conda install numpy        |   4.29386  |  4.98370 |  4.10029 | 4.99271 |
+-----------------------------------+------------+----------+----------+---------+

从结果来看，与参考文献中的任何一个numpy版本相比，我的代码的计时要慢一些。