Faster NumPy on Mac GPU with MLX
Accelerate your Numpy Scientific Workflows on Apple Silicon with MLX
In this video, I compare the execution speed of numpy and numba with MLX, a python library that executes code on Mac GPUs and provides a numpy-compatible API. For my tests, I simulate a large number time series that follow an AR(3)-GARCH(1,1) process and compute the t-stat for the H0 that mean returns are 0 for each sample path. Find out how MLX performs in comparison to numpy and numba!
Note: MLX requires Apple Silicon (M1/M2/M3).
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The sample code used in this video is available on GitHub: https://github.com/Vincent-Codes-Fina...
The written version of this tutorial is available at: https://vincent.codes.finance/posts/a...
🔖 Chapters:
00:00 Intro
00:37 What is MLX?
04:00 Sample numpy workflow
06:18 MLX code
08:32 Numba code
10:52 Benchmarking
13:57 Benchmark results
17:19 Outro
Video links:
MLX: https://ml-explore.github.io/mlx/buil...
numpy: https://numpy.org/
numba: https://numba.pydata.org/
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