Cited 0 time in
GPU-based matrix multiplication methods for social networks 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 | 2022-07-16T02:38:31Z | - |
| dc.date.available | 2022-07-16T02:38:31Z | - |
| dc.date.created | 2021-05-13 | - |
| dc.date.issued | 2014-10 | - |
| dc.identifier.issn | 0000-0000 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158946 | - |
| dc.description.abstract | A matrix multiplication is a building block for social networks analysis. Recently, there have been various methods proposed for GPU-based matrix multiplications. NVIDIA, one of major manufacturers of GPUs, has also proposed various matrix multiplication methods based on GPUs. In this paper, we introduce the methods, and evaluate their performance via extensive experiments using synthetic and real-world datasets. Our results would help practitioners choose the best one for analyzing real-world social networks. | - |
| dc.language | 영어 | - |
| dc.language.iso | en | - |
| dc.publisher | Association for Computing Machinery, Inc | - |
| dc.title | GPU-based matrix multiplication methods for social networks analysis | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
| dc.identifier.doi | 10.1145/2663761.2664192 | - |
| dc.identifier.scopusid | 2-s2.0-84910019931 | - |
| dc.identifier.bibliographicCitation | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014, pp.309 - 313 | - |
| dc.relation.isPartOf | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 | - |
| dc.citation.title | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 | - |
| dc.citation.startPage | 309 | - |
| dc.citation.endPage | 313 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Conference Paper | - |
| dc.description.journalClass | 1 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordPlus | Program processors | - |
| dc.subject.keywordPlus | Social networking (online) | - |
| dc.subject.keywordPlus | Building blockes | - |
| dc.subject.keywordPlus | CUDA | - |
| dc.subject.keywordPlus | GPU | - |
| dc.subject.keywordPlus | Gpu-based | - |
| dc.subject.keywordPlus | MAtrix multiplication | - |
| dc.subject.keywordPlus | Real-world | - |
| dc.subject.keywordPlus | Real-world datasets | - |
| dc.subject.keywordPlus | Social Networks Analysis | - |
| dc.subject.keywordPlus | Matrix algebra | - |
| dc.subject.keywordAuthor | CUDA | - |
| dc.subject.keywordAuthor | GPU | - |
| dc.subject.keywordAuthor | Matrix multiplication | - |
| dc.identifier.url | https://dl.acm.org/doi/10.1145/2663761.2664192 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1366
COPYRIGHT © 2024 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
