SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning - Lin, Shengle; Liu, Chubo; Ding, Yan; Zhou, Joey Tianyi; Li, Kenli; Yang, Wangdong (2025 62nd ACM/IEEE Design Automation Conference (DAC), 2025)
Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV algorithms to diverse matrices and architectures requires a framework capable of accurately recognizing sparse patterns and selecting the optimal implementation. In this work, we introduce Sparsity-aware SpMV (SSpMV), a framework that integrates expert-designed features with multimodal representations to adaptively predict the best-performing algorithm and parameters. For this purpose, we design a multimodal neural network called MM-Adapter, to capture diverse modalities to represent the computational features of SpMV. Experimental results demonstrate that MMAdapter achieves the highest accuracy of 81.05 %, outperforming existing SpMV prediction models. Furthermore, SSpMV consistently delivers substantial performance improvements over state-of-the-art sparse libraries across various multi-core platforms.