拿到新款 mbp 的 v 友，有兴趣的话能测一下 numpy scipy 的 benchmark 嘛

apple silicon 的支持还在修修补补阶段, 要用可以自己找 commit 去编译

本地们调试能画图就行，运算丢给服务器和超算了

@thedrwu 就是因为只在本地画图我才只关心 python ，有时候画点复杂的图还是需要点算力的😂

Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz

Dotted two 4096x4096 matrices in 0.34 s.
Dotted two vectors of length 524288 in 0.02 ms.
SVD of a 2048x1024 matrix in 1.03 s.
Cholesky decomposition of a 2048x2048 matrix in 0.61 s.
Eigendecomposition of a 2048x2048 matrix in 9.66 s.

16c mac pro / 96g

Dotted two 4096x4096 matrices in 0.28 s.
Dotted two vectors of length 524288 in 0.02 ms.
SVD of a 2048x1024 matrix in 0.56 s.
Cholesky decomposition of a 2048x2048 matrix in 0.07 s.
Eigendecomposition of a 2048x2048 matrix in 4.00 s.

我比较好奇的是，这么小规模的测试，误差很大吧...

@20015jjw 再跑了一次

Dotted two 4096x4096 matrices in 0.26 s.
Dotted two vectors of length 524288 in 0.02 ms.
SVD of a 2048x1024 matrix in 0.50 s.
Cholesky decomposition of a 2048x2048 matrix in 0.07 s.
Eigendecomposition of a 2048x2048 matrix in 3.77 s.

第三个相差都 10%了...
两次前后跑的，该跑的东西啥都没关

@20015jjw 差个 10%无所谓，主要是看有没有大于 10%的明显差距

16 寸低配 MBP M1 Pro 10 核

Dotted two 4096x4096 matrices in 0.56 s.
Dotted two vectors of length 524288 in 0.25 ms.
SVD of a 2048x1024 matrix in 0.67 s.
Cholesky decomposition of a 2048x2048 matrix in 0.08 s.
Eigendecomposition of a 2048x2048 matrix in 6.88 s.

@astrophys 有没有 MKL 还是很关键的，这种运输 openblas 的性能和 MKL 还是有差距的

@MongkeMary 是的呀，m1 的话就看有没有用 accelerate framework 了

@astrophys 在 r/python 上看到一个帖子，https://www.reddit.com/r/Python/comments/qog8x3/if_you_are_using_apples_m1_macs_compiling_numpy/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

暗影精灵 7
i7-11800H

Dotted two 4096x4096 matrices in 0.39 s.
Dotted two vectors of length 524288 in 0.05 ms.
SVD of a 2048x1024 matrix in 0.26 s.
Cholesky decomposition of a 2048x2048 matrix in 0.08 s.
Eigendecomposition of a 2048x2048 matrix in 2.57 s.

给大家做一个参考

M1 macbook air ，用的 accelerate 库编译的 numpy ，速度还可以。


Dotted two 4096x4096 matrices in 0.60 s.
Dotted two vectors of length 524288 in 0.11 ms.
SVD of a 2048x1024 matrix in 0.52 s.
Cholesky decomposition of a 2048x2048 matrix in 0.06 s.
Eigendecomposition of a 2048x2048 matrix in 5.98 s.

This was obtained using the following Numpy configuration:
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
NOT AVAILABLE
accelerate_info:
extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
blas_opt_info:
extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
NOT AVAILABLE
openblas_clapack_info:
NOT AVAILABLE
flame_info:
NOT AVAILABLE
lapack_opt_info:
extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
Supported SIMD extensions in this NumPy install:
baseline = NEON,NEON_FP16,NEON_VFPV4,ASIMD
found = ASIMDHP,ASIMDDP,ASIMDFHM
not found =