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October 20, 2025Open Access

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

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Proposed NM-SpMM enhances performance of matrix multiplication through N:M sparsity optimization, suggesting new programming approaches.

NM-SpMM achieves 2.1x speedup over nmSPARSE and up to 6.3x speedup over dense GEMM operations.
A hierarchical blocking mechanism is utilized to improve data locality, a critical factor for performance in GPGPU applications.
The NM-SpMM implementation is open source, allowing further exploration and adaptation of N:M sparsity techniques.
Challenges in programming and optimization for N:M sparsity are systematically addressed through the proposed performance analysis model.

Ma et al. (2025) studied this question.

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