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NumPy 2.5 speeds up `searchsorted` by up to 25×

blog.scientific-python.org 15 min read Editor score 8/10

A vectorized binary-search formulation was ported into NumPy 2.5 and measured at up to 25× faster than NumPy 2.4. The techniques may also inform performance work in scientific Python libraries that depend on search operations.

What: NumPy 2.5 includes a vectorized `searchsorted` implementation for batched queries against a static sorted array.
How: It tracks each search’s `lo` and `hi` in arrays and updates them together with NumPy operations, avoiding sequential Python searches.
Why it matters: The change achieved up to a 25× benchmark speedup and can benefit scientific libraries that use `searchsorted`.

The vectorized binary search combines a sorted array and query keys to produce insertion positions. Sketched from the article's own text.

Read on blog.scientific-python.org → Lobsters · 3 comments

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