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Speed-up NumPy 20x with Numexpr

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Speed-up NumPy 20x with Numexpr

Avi Chawla
Dec 28, 2022
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Numpy already offers fast and optimized vectorized operations. Yet, it does not support parallelism. This provides further scope for improving the run-time of NumPy.

To do so, use Numexpr. It allows you to speed up numerical computations with multi-threading and just-in-time compilation.

Depending upon the complexity of the expression, the speed-ups can range from 0.95x and 20x. Typically, it is expected to be 2x-5x.

Read more: Documentation.

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.

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