# NM-SpMM: Accelerating Matrix Multiplication Using N:M Sparsity with GPGPU

arXiv: https://arxiv.org/abs/2503.01253 · HTML: https://mlsanity.com/paper/2503.01253/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers](https://mlsanity.com/paper/2507.03117/) (2025)
- [TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks](https://mlsanity.com/paper/2505.23949/) (2025)
- [Motion Blur Robust Wheat Pest Damage Detection with Dynamic Fuzzy Feature Fusion](https://mlsanity.com/paper/2601.03046/) (2026)

## Abstract (arXiv)

Deep learning demonstrates effectiveness across a wide range of tasks. However, the dense and over-parameterized nature of these models results in significant resource consumption during deployment. In response to this issue, weight pruning, particularly through N:M sparsity matrix multiplication, offers an efficient solution by transforming dense operations into semi-sparse ones. N:M sparsity provides an option for balancing performance and model accuracy, but introduces more complex programming and optimization challenges. To address these issues, we design a systematic top-down performance analysis model for N:M sparsity. Meanwhile, NM-SpMM is proposed as an efficient general N:M sparsity implementation. Based on our performance analysis, NM-SpMM employs a hierarchical blocking mechanism as a general optimization to enhance data locality, while memory access optimization and pipeline design are introduced as sparsity-aware optimization, allowing it to achieve close-to-theoretical peak performance across different sparsity levels. Experimental results show that NM-SpMM is 2.1x faster than nmSPARSE (the state-of-the-art for general N:M sparsity) and 1.4x to 6.3x faster than cuBLAS's dense GEMM operations, closely approaching the theoretical maximum speedup resulting from the reduction in computation due to sparsity. NM-SpMM is open source and publicly available at https://github.com/M-H482/NM-SpMM.