getkeops/keops

Health v2 · maintenance only

Adoption not part of the score

1189 stars · 78 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

KeOps (pykeops) is a Python library for computing kernel reductions over large arrays on CPUs and GPUs using efficient C++/CUDA routines with automatic differentiation. It avoids materializing full kernel or distance matrices, enabling memory-efficient KNN queries, N-body interactions, and point cloud convolutions.

Use cases

• compute kernel matrix-vector products that don't fit in GPU memory
• fast K-nearest neighbors search on large point clouds
• N-body interaction simulations with gradients
• point cloud convolutions for geometric deep learning
• autodiff through kernel operations in PyTorch
• speed up Gaussian kernel computations by 10-100x over PyTorch

When to choose

• you need kernel or distance computations on millions of points without memory overflows
• you want autodiff-capable geometric operations in NumPy, PyTorch, Matlab, or R
• geometric deep learning or kernel methods are memory-bound in your pipeline

When to avoid

• you need simple dense linear algebra that standard BLAS/PyTorch handles fine
• your project requires pure Python without C++/CUDA compilation
• you work on small datasets where the overhead isn't worth it

Facets

library · maturity active

math machine-learning gpu-computing benchmarking machine-learning gpu-computing mathematics python cross-platform kernel-operations autodiff cuda pytorch numpy nearest-neighbors point-clouds symbolic-tensors algorithms gpu

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