Numpy shows some vector extensions are used, but not all of them: Supported SIMD extensions in this NumPy install:īaseline = NEON,NEON_FP16,NEON_VFPV4,ASIMD So, MKL is better on intel chips than OpenBLAS is on ARM64. The dual Xeon 6248R server I ssh into to actually run most of my code does the same in 82ms (~7x faster, with 48 cores instead of 8 P cores).
The actual linear algebra part of numpy - matmul, and so on, is multi-core just as it is with the mkl backend (or openblas).Ī=np.random.rand(4096,4096) takes 598 ms on the M1 pro to do the matmul, and all cores light up in htop (including the E-cores). Some cases ( mkl_fft and _rand and so on) are slower on the M1, because no analog using apple accelerate or veclib, as best I can tell, exists. Numerics performance (numpy) is generally about on par with my previous 15" MBP with the last generation of intel i9s apple used. You need the env var CONDA_SUBDIR=osx-arm64 set in your shell rc file or elsewhere to make sure conda only uses native code. I have a 16" M1 Pro machine, and use a commercial license of Conda.Īll of the packages that I particularly care about (numpy, scipy, astropy, skimage.
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