HetGraph: A High Performance CPU-CGRA Architecture for Matrix-based Graph Analytics

Abstract

In this paper, we explore graph analytics on a heterogeneous platform named HetGraph integrating with CPU and a flexible CGRA accelerator called RFU for matrix-based paradigm in this paper. RFU utilizes the lightweight pipeline without data hazards to support various generalized Sparse Matrix-Vector multiplications (SpMVs) of matrix-based graph analytics effectively. HetGraph utilizes the degree-aware workload distribution with vector-scanning sparsity removing scheme to alleviate the impact of highly sparse graph. Furthermore, we propose a heterogeneous work-stealing strategy to balance the workloads between CPU and RFU for HetGraph. To the best of our knowledge, HetGraph is the first heterogeneous CPU-CGRA architecture for matrix-based graph analytics. Overall, HetGraph achieves 9.42x, 2.45x speedup, and 9.80x, 7.70x energy savings on average compared to state-of-the-art (SOTA) CPU-based and GPGPU-based solutions respectively. Compared to the SOTA graph analytics accelerator, HetGraph also achieves 1.42x speedup and 1.06x less energy.