An Efficient and Scalable Distributed Hypergraph Processing System

• Item type: Trans(1)
• 公開日: 2021-11-25
• タイトル: An Efficient and Scalable Distributed Hypergraph Processing System
• タイトル / 言語: en
• タイトル: An Efficient and Scalable Distributed Hypergraph Processing System
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [通常論文] hypergraph, bipartite graph, distributed graph processing
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属: Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属: Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, The University of Tokyo
• 著者名: Shugo, Fujimura; Shigeyuki, Sato; Kenjiro, Taura
• 著者名(英): Shugo, Fujimura; Shigeyuki, Sato; Kenjiro, Taura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The recent increase in the amount of graph data has drawn our attention to distributed graph processing systems scalable to large-scale inputs. Although distributed-memory processing is generally less efficient than shared-memory processing because of the communication costs and program complexity, state-of-the-art distributed graph processing systems, such as Gemini, have achieved a comparable efficiency by using lightweight graph partitioning and load balancing. However, the achievement of both scalability and efficiency in hypergraph processing remains an open issue because distributed hypergraph processing systems have not been extensively studied. In this paper, we propose a distributed hypergraph processing system based on Gemini that achieves both scalability and efficiency. Our system outperformed the state-of-the-art shared-memory hypergraph processing system Hygra from several folds to tens of folds on a single-node computer. In addition, it showed speedup in processing a large-scale dataset on a multi-node computer.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The recent increase in the amount of graph data has drawn our attention to distributed graph processing systems scalable to large-scale inputs. Although distributed-memory processing is generally less efficient than shared-memory processing because of the communication costs and program complexity, state-of-the-art distributed graph processing systems, such as Gemini, have achieved a comparable efficiency by using lightweight graph partitioning and load balancing. However, the achievement of both scalability and efficiency in hypergraph processing remains an open issue because distributed hypergraph processing systems have not been extensively studied. In this paper, we propose a distributed hypergraph processing system based on Gemini that achieves both scalability and efficiency. Our system outperformed the state-of-the-art shared-memory hypergraph processing system Hygra from several folds to tens of folds on a single-node computer. In addition, it showed speedup in processing a large-scale dataset on a multi-node computer.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464814
• 書誌情報: 情報処理学会論文誌プログラミング（PRO）; 巻 14, 号 5, 発行日 2021-11-25
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7802
• 出版者 / 言語: ja
• 出版者: 情報処理学会