research-article

LCCG: a locality-centric hardware accelerator for high throughput of concurrent graph processing

Authors:

Haikun LiuAuthors Info & Claims

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 45, Pages 1 - 14

https://doi.org/10.1145/3458817.3480854

Published: 13 November 2021 Publication History

Abstract

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a <u>L</u>ocality-<u>C</u>entric programmable accelerator that augments the many-core processor for achieving higher throughput of <u>C</u>oncurrent <u>G</u>raph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. Moreover, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).

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Cited By

Mao FLiu XZhang YLiu HLiao XJin HZhang WZhou JWu YNie LGuo YJiang ZLiu J(2024)PMGraph: Accelerating Concurrent Graph Queries over Streaming GraphsACM Transactions on Architecture and Code Optimization10.1145/368933721:4(1-25)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689337
Yu HZhang YHe LZhao YLi XXin RZhao JLiao XLiu HHe BJin H(2024)RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00094(1264-1277)Online publication date: 2-Nov-2024
https://doi.org/10.1109/MICRO61859.2024.00094
Wang SXu HMamandipoor AMahapatra RAhn BGhodrati SKailas KAlian MEsmaeilzadeh H(2024)Data Motion Acceleration: Chaining Cross-Domain Multi Accelerators2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00083(1043-1062)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00083
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Index Terms

LCCG: a locality-centric hardware accelerator for high throughput of concurrent graph processing
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Data flow architectures
      2. Special purpose systems
    2. Parallel architectures
      1. Multicore architectures

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cover image ACM Conferences

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2021

1493 pages

ISBN:9781450384421

DOI:10.1145/3458817

General Chair:
Bronis R. de Supinski,
Program Chairs:
Mary Hall,
Todd Gamblin

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Published: 13 November 2021

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SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 19, 2021

Missouri, St. Louis

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Cited By

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https://dl.acm.org/doi/10.1145/3689337
Yu HZhang YHe LZhao YLi XXin RZhao JLiao XLiu HHe BJin H(2024)RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00094(1264-1277)Online publication date: 2-Nov-2024
https://doi.org/10.1109/MICRO61859.2024.00094
Wang SXu HMamandipoor AMahapatra RAhn BGhodrati SKailas KAlian MEsmaeilzadeh H(2024)Data Motion Acceleration: Chaining Cross-Domain Multi Accelerators2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00083(1043-1062)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00083
Xu XWang FJiang HCheng YFeng DFang P(2024)A disk I/O optimized system for concurrent graph processing jobsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-2361-018:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11704-023-2361-0
Tan HChen XChen YHe BWong W(2023)LightRW: FPGA Accelerated Graph Dynamic Random WalksProceedings of the ACM on Management of Data10.1145/35889441:1(1-27)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588944
Yin XZhao ZGupta RAamodt TJerger NSwift M(2023)Glign: Taming Misaligned Graph Traversals in Concurrent Graph ProcessingProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3567955.3567963(78-92)Online publication date: 25-Mar-2023
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