Cuper: Customized Dataflow and Perceptual Decoding for Sparse Matrix-Vector Multiplication on HBM-Equipped FPGAs
Enxin Yi, Yiru Duan, Yinuo Bai, Kang Zhao, Zhou Jin, Weifeng Liu
Abstract
Sparse matrix-vector multiplication (<tex>$S$</tex>pMV) is pivotal in many scientific computing and engineering applications. Considering the memory-intensive nature and irregular data access patterns inherent in SpMV, its acceleration is typically bounded by the limited bandwidth. Multiple memory channels of the emerging high bandwidth memory (HBM) provide exceptional bandwidth, offering a great opportunity to boost the performance of SpMV. However, ensuring high bandwidth utilization with low memory access conflicts is still non-trivial. In this paper, we present Cuper, a high-performance SpMV accelerator on HBM-equipped FPGAs. Through customizing the dataflow to be HBM-compatible with the proposed sparse storage format, the bandwidth utilization can be sufficiently enhanced. Furthermore, a two-step reordering algorithm and perceptual decoder-centric hardware architecture are designed to greatly mitigate read-after-write (RAW) conflicts, enhance the vector reusability and on-chip memory utilization. The evaluation of 12 large matrices shows that Cuper's geomean throughput outperforms the four latest SpMV accelerators HiSparse, GraphLily, Sextans, and Serpens, by 3.28×, 1.99×, 1.75×, and 1.44×, respectively. Furthermore, the geomean bandwidth efficiency shows 3.28×, 2.20×, 2.82×, and 1.31x improvements, while the geomean energy efficiency has 3.59×, 2.08×, 2.21×, and 1.44× optimizations, respectively. Cuper also demonstrates 2.51× throughput and 7.97× energy efficiency of improvement over the K80 GPU on 2,757 SuiteSparse matrices.