# A Practical GPU-Accelerated Implementation of Orthogonal Matching Pursuit

arXiv: https://arxiv.org/abs/2407.06434 · HTML: https://mlsanity.com/paper/2407.06434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries](https://mlsanity.com/paper/2412.08890/) (2024)

## Abstract (arXiv)

Finding the sparsest solution to the underdetermined system y=Ax, given a tolerance, is known to be NP-hard. Many approximate solutions to this problem exist, and Orthogonal Matching Pursuit (OMP) is one of the most widely used. However, existing OMP implementations don't take full advantage of matrix properties or modern CPU and GPU-based Linear Algebra kernels. For this paper, we implemented an efficient implementation of OMP that leverages Cholesky inverse properties as well as the power of GPUs to deliver up to 310x speedup over Scikit-Learn and 26x over SPAMS. The package is published on PyPI (textttpip install batched-omp) and is fully scikit-learn compatible.