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.

We present ASM-SpMM, a high-performance SpMM library co-designed with ARM SME. ASM-SpMM combines a memory-efficient compression format, an SME-aware prefetching kernel optimized for outer-product execution, a hybrid matrix–vector execution strategy, and work-stealing-based dynamic load balancing across heterogeneous cores. Experiments on emerging Armv9 platforms demonstrate up to 7.9× speedup over state-of-the-art SpMM libraries across diverse matrices. A GNN inference case study further shows that ASM-SpMM significantly improves end-to-end performance over widely used GNN frameworks, highlighting the effectiveness of SME-aware SpMM optimization on ARM CPUs.