SPLIM: Bridging the Gap Between Unstructured SpGEMM and Structured In-Situ Computing

Sparse matrix-matrix multiplication (SpGEMM) is a critical kernel widely employed in machine learning and graph algorithms. However, high sparsity of real-world matrices makes SpGEMM memory-intensive. In-situ computing offers the potential to accelerate memory-intensive applications through high bandwidth and parallelism. Nevertheless, the irregular distribution of nonzeros renders software SpGEMM computation unstructured. In contrast, in-situ hardware platforms follow a fixed computation pattern, making them structured. The mismatch between unstructured software and structured hardware leads to suboptimal performance of current solutions. In this article, we propose SPLIM, a novel in-situ computing SpGEMM accelerator. SPLIM involves two innovations. First, we present a novel computation paradigm that converts SpGEMM into structured in-situ multiplication and unstructured accumulation. Second, we develop a unique coordinates alignment method utilizing in-situ search operations, effectively transforming unstructured accumulation into highly parallel search operations. Our experimental results demonstrate that SPLIM achieves $276\times $ performance improvement and $687\times $ energy saving compared to NVIDIA RTX A6000 GPU.