HighP: In-Memory Acceleration of SpGEMM With High Bank-Level Parallelism

Generalized sparse matrix-matrix multiplication (SpGEMM) is a critical computational primitive that is highly memory-bound due to its inherent irregular data-dependent access pattern. Near-bank processing-in-memory (PIM) is a promising technique to overcome the memory bottleneck of SpGEMM by performing computations near the bank where the data is stored. However, earlier PIM studies fail to fully utilize the high memory bandwidth when performing SpGEMM due to low bank-level parallelism.

As a result, 80% memory bandwidth is wasted as observed in our in-depth experimental analysis.Our key insight in this paper is that non-conflicting matrix columns in SpGEMM, where each row of these columns has no more than one non-zero element, can be processed simultaneously in different banks. We hence propose HighP, a near-bank PIM accelerator for SpGEMM with high bank-level parallelism. We first propose a set-based search mechanism, which finds non-conflicting columns through set operations automatically.

We then develop a DIMM-based PIM architecture with detailed hardware and workflow designs for SpGEMM. Set operation logic and unified scratchpad memory management are designed to perform set operations with high computational parallelism and to enhance data reuse, respectively. HighP provides up to 17.88× performance improvement compared to the state-of-th-eart SpGEMM accelerator and achieves up to 8.19× performance improvement over the state-of-the-art PIM solution.