Sparse-Sparse Matrix Multiplication Accelerator on FPGA featuring Distribute-Merge Product Dataflow
Yuta Nagahara, Jiale Yan, Kazushi Kawamura, Masato Motomura, Thiem Van Chu · 2024
Sparse-Sparse matrix multiplication (SpMSpM) is a critical computation in various fields such as computational science and graph analysis. It poses computational challenges for general-purpose CPUs and GPUs due to its requirements for random memory access and the inherently low spatial/temporal locality. Given the increasing importance of SpMSpM, numerous accelerators have been recently proposed. However, they suffer from various issues such as low input utilization, heavy computational load, and excessive memory traffic during the merging process of intermediate results. This paper introduces a novel Distribute-Merge Product (DMP) SpMSpM dataflow and a DMP-based SpMSpM Architecture (DMSA). DMP distributes the workload into balanced streams, generates partial matrices based on these streams, and merges the partial results in a parallel and pipelined fashion. We have designed DMSA as a highly scalable architecture, implemented it on a Xilinx ZCU106 Evaluation Kit, and evaluated it on a set of benchmarks from the SuiteSparse matrix collection. When compared to a latest SpMSpM accelerator with approximately the same amount of hardware resources on the same FPGA platform, DMSA achieves 2.72 × speedup, by facilitating the parallelism of partial matrix generation and merging. The speedup on the same platform reaches 4.80 × when the parallelism explored in the merging process is doubled, evidencing the DMSA’s superb scalability.