ASaP: Automatic Software Prefetching for Sparse Tensor Computations in MLIR

Paper i proceeding, 2025

Sparse tensor computations suffer from irregular memory access patterns that degrade cache performance. While software prefetching can mitigate this, existing compiler approaches lack the semantic insight needed for effective optimization. We present ASaP, an automatic software prefetching framework integrated within MLIR’s sparse tensor dialect. By leveraging semantic information-tensor formats and loop structure-available during sparsification, ASaP determines accurate buffer bounds and injects prefetches in both innermost and outer loops, achieving broader coverage than prior work. Evaluated on SuiteSparse matrices, ASaP demonstrates significant performance gains for unstructured matrices. For SpMV with innermost-loop prefetching, ASaP achieves 1.38× speedup over Ainsworth & Jones. For SpMM with outer-loop prefetching, ASaP achieves 1.28× speedup while Ainsworth & Jones fails to generate prefetches. Our experiments reveal that disabling inaccurate hardware prefetchers frees critical resources for software prefetching, suggesting future architectures should expose prefetcher control as an optimization interface.