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Regular Paper

Skyway: Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management

Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100045, China
Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, U.S.A.
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Abstract

Graph processing is a vital component of many AI and big data applications. However, due to its poor locality and complex data access patterns, graph processing is also a known performance killer of AI and big data applications. In this work, we propose to enhance graph processing applications by leveraging fine-grained memory access patterns with a dual-path architecture on top of existing software-based graph optimizations. We first identify that memory accesses to the offset, edge, and state array have distinct locality and impact on performance. We then introduce the Skyway architecture, which consists of two primary components: 1) a dedicated direct data path between the core and memory to transfer state array elements efficiently, and 2) a data-type aware fine-grained memory-side row buffer hardware for both the newly designed direct data path and the regular memory hierarchy data path. The proposed Skyway architecture is able to improve the overall performance by reducing the memory access interference and improving data access efficiency with a minimal overhead. We evaluate Skyway on a set of diverse algorithms using large real-world graphs. On a simulated four-core system, Skyway improves the performance by 23% on average over the best-performing graph-specialized hardware optimizations.

Keywords

graph applicationcomputer architecturememory hierarchy

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Journal of Computer Science and Technology
Volume 39 Issue 4,
August 2024
Pages 871-894
DOI: 10.1007/s11390-023-2939-x
Cite this article:
Zou M, Zhang M-Z, Wang R-J, et al. Skyway: Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management. Journal of Computer Science and Technology, 2024, 39(4): 871-894. https://doi.org/10.1007/s11390-023-2939-x

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Received: 28 October 2022
Accepted: 14 October 2023
Published: 20 September 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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