poster

RealGraph+: A High-Performance Single-Machine-Based Graph Engine that Utilizes IO Bandwidth Effectively

Authors:

Myung-Hwan Jang,

Jeong-Min Park,

Sang-Wook KimAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 276 - 279

https://doi.org/10.1145/3543873.3587365

Published: 30 April 2023 Publication History

Abstract

This paper proposes RealGraph+, an improved version of RealGraph that processes large-scale real-world graphs efficiently in a single machine. Via a preliminary analysis, we observe that the original RealGraph does not fully utilize the IO bandwidth provided by NVMe SSDs, a state-of-the-art storage device. In order to increase the IO bandwidth, we equip RealGraph+ with three optimization strategies to issue more-frequent IO requests: (1) User-space IO, (2) Asynchronous IO, and (3) SIMD processing. Via extensive experiments with four graph algorithms and six real-world datasets, we show that (1) each of our strategies is effective in increasing the IO bandwidth, thereby reducing the execution time; (2) RealGraph+ with all of our strategies improves the original RealGraph significantly; (3) RealGraph+ outperforms state-of-the-art single-machine-based graph engines dramatically; (4) it shows performance comparable to or even better than those of other distributed-system-based graph engines.

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Cited By

Wang RZong WHe SChen XLi ZDang ZBagchi SZhang Y(2024)Efficient large graph processing with chunk-based graph representation modelProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692067(1239-1255)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692067
Jiang QJia LWang C(2024)GNNDrive: Reducing Memory Contention and I/O Congestion for Disk-based GNN TrainingProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673063(650-659)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673063
Park JJang MBae DKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)RealGraphGPU++: A High-Performance GPU-Based Graph Engine with Direct Storage-to-DM IOCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651549(654-657)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651549
Show More Cited By

Index Terms

RealGraph+: A High-Performance Single-Machine-Based Graph Engine that Utilizes IO Bandwidth Effectively
1. Information systems
  1. Data management systems

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Information

Published In

cover image ACM Conferences

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

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Author Tags

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Poster
Research
Refereed limited

Funding Sources

Samsung Electronics Co., Ltd
The Korea government (Ministry of Science and ICT)

Conference

WWW '23

Sponsor:

SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Wang RZong WHe SChen XLi ZDang ZBagchi SZhang Y(2024)Efficient large graph processing with chunk-based graph representation modelProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692067(1239-1255)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692067
Jiang QJia LWang C(2024)GNNDrive: Reducing Memory Contention and I/O Congestion for Disk-based GNN TrainingProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673063(650-659)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673063
Park JJang MBae DKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)RealGraphGPU++: A High-Performance GPU-Based Graph Engine with Direct Storage-to-DM IOCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651549(654-657)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651549
Park JJang MKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)RealGraphGPUWeb: A Convenient and Efficient GPU-Based Graph Analysis Platform on the WebCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651237(1011-1014)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651237

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