RealGraph-GPU: A High-Performance GPU-Based Graph Engine toward Large-Scale Real-World Network Analysis
- Authors
- Jang, Myung-Hwan; Ko, Yunyong; Jeong, Dongkyu; Park, Jeong-Min; Kim, Sang-Wook
- Issue Date
- Oct-2022
- Publisher
- ACM CIKM 2022
- Keywords
- Abstract A graph; consisting of vertices and edges; has been widely adopted for network analysis. Recently; with the increasing size of real-world networks; many graph engines have been studied to efficiently process large-scale real-world graphs. RealGraph; one of the state-of-the-art single-machine-based graph engines; efficiently processes storage-to-memory I/Os by considering unique characteristics of real-world graphs. Via an in-depth analysis of RealGraph; however; we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this; in this paper; we propose RealGraphGPU; a GPU-based single-machine graph engine. We design the core components required for GPU-based graph processing and incorporate them into the architecture of RealGraph. Further; we propose two optimizations that successfully address the technical issues that could cause the performance degradation in the GPU-based
- Citation
- ACM Conference on Information and Knowledge Management, pp.4074 - 4078
- Indexed
- OTHER
- Journal Title
- ACM Conference on Information and Knowledge Management
- Start Page
- 4074
- End Page
- 4078
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188585
- DOI
- 10.1145/3511808.3557679
- Abstract
- A graph, consisting of vertices and edges, has been widely adopted for network analysis. Recently, with the increasing size of realworld networks, many graph engines have been studied to efficiently process large-scale real-world graphs. RealGraph, one of the state-of-the-art single-machine-based graph engines, efficiently processes storage-to-memory I/Os by considering unique characteristics of real-world graphs. Via an in-depth analysis of RealGraph, however, we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this, in this paper, we propose RealGraphGPU, a GPU-based single-machine graph engine. We design the core components required for GPU-based graph processing and incorporate them into the architecture of RealGraph. Further, we propose two optimizations that successfully address the technical issues that could cause the performance degradation in the GPU-based graph engine: buffer pre-checking and edge-based workload allocation strategies. Through extensive evaluation with 6 real-world datasets, we demonstrate that (1) RealGraphGPU improves RealGraph by up to 546%, (2) RealGraphGPU outperforms existing state-of-the-art graph engines dramatically, and (3) the optimizations are all effective in large-scale graph processing.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188585)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.