HPN-SpGEMM: Hybrid PIM-NMP for SpGEMM

Abstract

Sparse matrix-matrix multiplication (SpGEMM) is widely used in various scientific computing applications. However, the performance of SpGEMM is typically bound by memory performance due to irregular access patterns. Prior accelerators leveraging high-bandwidth memory (HBM) with optimized data flows still face limitations in handling sparse matrices with varying sizes and sparsity levels. We propose HPN-SpGEMM, a hybrid architecture that employs both processing-in-memory (PIM) cores inside bank groups and near-memory-processing (NMP) cores in the logic die of an HBM memory. To the best of our knowledge, this is the first hybrid architecture for SpGEMM that leverages both PIM cores and NMP cores. Evaluation results demonstrate significant performance gains, effectively overcoming memory-bound constraints.