Efficient sparse matrix multiplication on GPU for large social network analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Yong-Yeon | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Bae, Duck-Ho | - |
dc.date.accessioned | 2022-07-15T20:45:59Z | - |
dc.date.available | 2022-07-15T20:45:59Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/156189 | - |
dc.description.abstract | As a number of social network services appear online recently, there have been many attempts to analyze social networks for extracting valuable information. Most existing methods first represent a social network as a quite sparse adjacency matrix, and then analyze it through matrix operations such as matrix multiplication. Due to the large scale and high complexity, efficient processing multiplications is an important issue in social network analysis. In this paper, wepropose aGPU-based method for efficient sparse matrix multiplication through the parallel computing paradigm. The proposed method aims at balancing the amount of workload both at fine- and coarse-grained levels for maximizing the degree of parallelism in GPU. Through extensive experiments using synthetic and real-world datasets, we show that the proposed method outperforms previous methods by up to three orders-of-magnitude. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Efficient sparse matrix multiplication on GPU for large social network analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/2806416.2806445 | - |
dc.identifier.scopusid | 2-s2.0-84958252001 | - |
dc.identifier.bibliographicCitation | International Conference on Information and Knowledge Management, Proceedings, v.19-23-Oct-2015, pp.1261 - 1270 | - |
dc.relation.isPartOf | International Conference on Information and Knowledge Management, Proceedings | - |
dc.citation.title | International Conference on Information and Knowledge Management, Proceedings | - |
dc.citation.volume | 19-23-Oct-2015 | - |
dc.citation.startPage | 1261 | - |
dc.citation.endPage | 1270 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Complex networks | - |
dc.subject.keywordPlus | Knowledge management | - |
dc.subject.keywordPlus | Parallel algorithms | - |
dc.subject.keywordPlus | Social networking (online) | - |
dc.subject.keywordPlus | Adjacency matrices | - |
dc.subject.keywordPlus | Degree of parallelism | - |
dc.subject.keywordPlus | MAtrix multiplication | - |
dc.subject.keywordPlus | Parallel com- puting | - |
dc.subject.keywordPlus | Real-world datasets | - |
dc.subject.keywordPlus | Social network services | - |
dc.subject.keywordPlus | Sparse matrices | - |
dc.subject.keywordPlus | Three orders of magnitude | - |
dc.subject.keywordPlus | Matrix algebra | - |
dc.subject.keywordAuthor | GPU | - |
dc.subject.keywordAuthor | Social network analysis | - |
dc.subject.keywordAuthor | Sparse matrix multiplication | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2806416.2806445 | - |
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