A Survey on Graph Processing Accelerators: Challenges and Opportunities

TL;DR

This survey reviews the design, implementation, and evaluation of graph processing accelerators, highlighting the diversity of approaches and the challenges faced in optimizing performance and energy efficiency.

Contribution

It provides a comprehensive overview of current techniques and benchmarks in graph accelerators, identifying gaps and future opportunities in the field.

Findings

No single best solution for all graph processing tasks

Diverse hardware configurations impact accelerator performance

Benchmark results vary across different accelerator designs

Abstract

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerator. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and complexity of hardware configurations. We finially present to discuss several challenges in details, and to further explore the opportunities for the future research.