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

[astrophys]

测试脚本： https://gist.github.com/markus-beuckelmann/8bc25531b11158431a5b09a45abd6276

很好奇这一代 M1 Pro Max 在 Python 科学计算上的提升有多大，之前 v 友测的上一代 M1 的算力在不谈功耗的情况下大概和 i5 互有胜负： https://v2ex.com/t/733777

[rpman]

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

[thedrwu]

本地们调试能画图就行，运算丢给服务器和超算了

[astrophys]

@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😂

[yangbin9317]

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.

[20015jjw]

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]

@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

[astrophys]

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

[MongkeMary]

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.

[MongkeMary]

@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

[astrophys]

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

[Aspector]

@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

[shinecurve]

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

[lqcc]

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 =