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PPoPP2026顶会

ASM-SpMM: Unleashing the Potential of Arm SME for Sparse Matrix Multiplication Acceleration

Jiazhi Jiang, Xijia Yao, Jiayu Chen, Jinhui Wei, Dan Huang, Yutong Lu

2026年份

摘要

Sparse Matrix–Matrix Multiplication (SpMM) is a core kernel in scientific computing, data analytics, and artificial intelligence, supporting applications such as linear solvers and Graph Neural Networks (GNNs). The Scalable Matrix Extension (SME) in Armv9 introduces dedicated matrix acceleration for ARM CPUs, but exploiting its full potential for SpMM requires architecture-aware optimizations to address irregular sparsity and hardware constraints.

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