{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T16:48:56Z","timestamp":1786639736898,"version":"build-2736575974"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,9]]},"abstract":"<jats:p>As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform a rebuild of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper, we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore, we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.<\/jats:p>","DOI":"10.14778\/3151113.3151122","type":"journal-article","created":{"date-parts":[[2017,10,19]],"date-time":"2017-10-19T12:30:08Z","timestamp":1508416208000},"page":"107-120","source":"Crossref","is-referenced-by-count":71,"title":["Accelerating dynamic graph analytics on GPUs"],"prefix":"10.14778","volume":"11","author":[{"given":"Mo","family":"Sha","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Apache flink. https:\/\/flink.apache.org\/. Accessed: 2016-10-18.  Apache flink. https:\/\/flink.apache.org\/. Accessed: 2016-10-18."},{"key":"e_1_2_1_2_1","unstructured":"Cusp library. https:\/\/developer.nvidia.com\/cusp. Accessed: 2017-03-25.  Cusp library. https:\/\/developer.nvidia.com\/cusp. Accessed: 2017-03-25."},{"key":"e_1_2_1_3_1","unstructured":"cusparse. https:\/\/developer.nvidia.com\/cusparse. Accessed: 2016-11-09.  cusparse. https:\/\/developer.nvidia.com\/cusparse. Accessed: 2016-11-09."},{"key":"e_1_2_1_4_1","volume-title":"https:\/\/nvlabs.github.io\/cub\/","author":"CUDA","year":"2015","unstructured":"CUDA UnBound (CUB) library. https:\/\/nvlabs.github.io\/cub\/ , 2015 . CUDA UnBound (CUB) library. https:\/\/nvlabs.github.io\/cub\/, 2015."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2014.69"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2597652.2597678"},{"key":"e_1_2_1_8_1","volume-title":"Stinger: Spatio-temporal interaction networks and graphs (sting) extensible representation","author":"Bader D. A.","year":"2009","unstructured":"D. A. Bader , J. Berry , A. Amos-Binks , D. Chavarr\u00eda-Miranda , C. Hastings , K. Madduri , and S. C. Poulos . Stinger: Spatio-temporal interaction networks and graphs (sting) extensible representation . Georgia Institute of Technology , Tech . Rep, 2009 . D. A. Bader, J. Berry, A. Amos-Binks, D. Chavarr\u00eda-Miranda, C. Hastings, K. Madduri, and S. C. Poulos. Stinger: Spatio-temporal interaction networks and graphs (sting) extensible representation. Georgia Institute of Technology, Tech. Rep, 2009."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539701389956"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1292609.1292616"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742783"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2014.2330597"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168836.2168846"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40450-4_29"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2012.04.007"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539701398363"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2014.45"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2012.6408680"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1921659.1921666"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2005.09.013"},{"key":"e_1_2_1_22_1","volume-title":"MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs. A High Level API for Fast Development of High Performance Graph Analytics on GPUs. ACM","author":"Fu Z.","year":"2014","unstructured":"Z. Fu , M. Personick , and B. Thompson . MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs. A High Level API for Fast Development of High Performance Graph Analytics on GPUs. ACM , New York , New York, USA , June 2014 . Z. Fu, M. Personick, and B. Thompson. MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs. A High Level API for Fast Development of High Performance Graph Analytics on GPUs. ACM, New York, New York, USA, June 2014."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367570"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.14778\/3151113.3151121"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/1782174.1782200"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/800113.803631"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2960414.2960419"},{"key":"e_1_2_1_28_1","first-page":"309","volume-title":"NSDI","author":"Iyer A. P.","year":"2015","unstructured":"A. P. Iyer , L. E. Li , and I. Stoica . Celliq : Real-time cellular network analytics at scale . In NSDI , pages 309 -- 322 , 2015 . A. P. Iyer, L. E. Li, and I. Stoica. Celliq : Real-time cellular network analytics at scale. In NSDI, pages 309--322, 2015."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2016.106"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41321-1_4"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2898361"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08608-8_7"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882959"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1837274.1837289"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/IMCSIT.2010.5680039"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2627692.2627694"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2717511"},{"key":"e_1_2_1_38_1","volume-title":"Introducing the graph 500","author":"Murphy R. C.","year":"2010","unstructured":"R. C. Murphy , K. B. Wheeler , B. W. Barrett , and J. A. Ang . Introducing the graph 500 . 2010 . R. C. Murphy, K. B. Wheeler, B. W. Barrett, and J. A. Ang. Introducing the graph 500. 2010."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783297"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-4655(89)90149-5"},{"key":"e_1_2_1_41_1","unstructured":"D. Sayce. 10 billions tweets number of tweets per day. http:\/\/www.dsayce.com\/social-media\/10-billions-tweets\/. Accessed: 2016-10-18.  D. Sayce. 10 billions tweets number of tweets per day. http:\/\/www.dsayce.com\/social-media\/10-billions-tweets\/. Accessed: 2016-10-18."},{"key":"e_1_2_1_42_1","volume-title":"Technical report: Accelerating dynamic graph analytics on gpus. arXiv preprint arXiv:1709.05061","author":"Sha M.","year":"2017","unstructured":"M. Sha , Y. Li , B. He , and K.-L. Tan . Technical report: Accelerating dynamic graph analytics on gpus. arXiv preprint arXiv:1709.05061 , 2017 . M. Sha, Y. Li, B. He, and K.-L. Tan. Technical report: Accelerating dynamic graph analytics on gpus. arXiv preprint arXiv:1709.05061, 2017."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2010.5470817"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1107499.1107504"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/InPar.2012.6339605"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915223"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2595641"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557111"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2367589.2367596"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2688500.2688538"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3067421.3067429"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2692916.2555255"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/1938545.1938548"},{"key":"e_1_2_1_54_1","volume-title":"Tracking influential nodes in dynamic networks. arXiv preprint arXiv:1602.04490","author":"Yang Y.","year":"2016","unstructured":"Y. Yang , Z. Wang , J. Pei , and E. Chen . Tracking influential nodes in dynamic networks. arXiv preprint arXiv:1602.04490 , 2016 . Y. Yang, Z. Wang, J. Pei, and E. Chen. Tracking influential nodes in dynamic networks. arXiv preprint arXiv:1602.04490, 2016."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522737"},{"key":"e_1_2_1_56_1","volume-title":"Approximate personalized pagerank on dynamic graphs. arXiv preprint arXiv:1603.07796","author":"Zhang H.","year":"2016","unstructured":"H. Zhang , P. Lofgren , and A. Goel . Approximate personalized pagerank on dynamic graphs. arXiv preprint arXiv:1603.07796 , 2016 . H. Zhang, P. Lofgren, and A. Goel. Approximate personalized pagerank on dynamic graphs. arXiv preprint arXiv:1603.07796, 2016."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPP.2011.22"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2013.111"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3151113.3151122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:34:34Z","timestamp":1672223674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3151113.3151122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,9]]}},"alternative-id":["10.14778\/3151113.3151122"],"URL":"https:\/\/doi.org\/10.14778\/3151113.3151122","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2017,9]]}}}