PEFP: Efficient k-hop Constrained s-t Simple Path Enumeration on FPGA

TL;DR

This paper introduces PEFP, an FPGA-based algorithm for efficient k-hop constrained s-t simple path enumeration, outperforming existing algorithms significantly through novel hardware and preprocessing techniques.

Contribution

The paper presents the first FPGA-based solution for k-hop constrained s-t simple path enumeration, incorporating preprocessing, batching, caching, and dataflow optimization techniques.

Findings

PEFP outperforms state-of-the-art JOIN algorithm by over 10x on average.

Preprocessing time and query processing are significantly reduced.

The FPGA implementation achieves high scalability and efficiency.

Abstract

Graph plays a vital role in representing entities and their relationships in a variety of fields, such as e-commerce networks, social networks and biological networks. Given two vertices s and t, one of the fundamental problems in graph databases is to investigate the relationships between s and t. A well-studied problem in such area is k-hop constrained s-t simple path enumeration. Nevertheless, all existing algorithms targeting this problem follow the DFS-based paradigm, which cannot scale up well. Moreover, using hardware devices like FPGA to accelerate graph computation has become popular. Motivated by this, in this paper, we propose the first FPGA-based algorithm PEFP to solve the problem of k-hop constrained s-t simple path enumeration efficiently. On the host side, we propose a preprocessing algorithm Pre-BFS to reduce the graph size and search space. On the FPGA side in PEFP, we propose a novel DFS-based batching technique to save on-chip memory efficiently. In addition, we also propose caching techniques to cache necessary data in BRAM, which overcome the latency bottleneck brought by the read/write operations from/to FPGA DRAM. Finally, we propose a data separation technique to enable dataflow optimization for the path verification module; hence the sub-stages in that module can be executed in parallel. Comprehensive experiments show that PEFP outperforms the state-of-the-art algorithm JOIN by more than 1 order of magnitude by average, and up to 2 orders of magnitude in terms of preprocessing time, query processing time and total time, respectively.