Work-in-Progress: HeteroRW: A Generalized and Efficient Framework for Random Walks in Graph Analysis

Abstract

Random walk (RW) is a common graph analysis algorithm that consists of two phases: construction and sampling. The construction phase is responsible for generating the sampling table. The sampling phase contains many walkers which wander through the whole graph to sample. However, RW is notorious for its dynamic and sparse memory access pattern, which makes existing research suffer low throughput and memory bottleneck. In addition, the variety of RW algorithms in different scenarios also brings new design challenges.This paper proposes HeteroRW, a generalized framework to accelerate RWs on FPGAs. HeteroRW first identifies the two phases’ computation characteristics and presents corresponding hardware acceleration designs, respectively. Then, HeteroRW achieves the template-based design to support a variety of RW algorithms. Finally, HeteroRW integrates a novel scheduling layer to partition the input data and perform design space exploration (DSE). Experimental results show that HeteroRW achieves 4.3x speedup over the recent FPGA implementation while effectively simplifying the accelerator customization process.