{"title":"多gpu系统上的可伸缩中间性中心","authors":"M. Bernaschi, Giancarlo Carbone, Flavio Vella","doi":"10.1145/2903150.2903153","DOIUrl":null,"url":null,"abstract":"Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing techniques to provide results in a reasonable amount of time. Our approach combines bi-dimensional (2-D) decomposition of the graph and multi-level parallelism together with a suitable data-thread mapping that overcomes most of the difficulties caused by the irregularity of the computation on GPUs. In order to reduce time and space requirements of BC computation, a heuristics based on 1-degree reduction technique is developed as well. Experimental results on synthetic and real-world graphs show that the proposed techniques are well suited to compute BC scores in graphs which are too large to fit in the memory of a single computational node.","PeriodicalId":226569,"journal":{"name":"Proceedings of the ACM International Conference on Computing Frontiers","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Scalable betweenness centrality on multi-GPU systems\",\"authors\":\"M. Bernaschi, Giancarlo Carbone, Flavio Vella\",\"doi\":\"10.1145/2903150.2903153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing techniques to provide results in a reasonable amount of time. Our approach combines bi-dimensional (2-D) decomposition of the graph and multi-level parallelism together with a suitable data-thread mapping that overcomes most of the difficulties caused by the irregularity of the computation on GPUs. In order to reduce time and space requirements of BC computation, a heuristics based on 1-degree reduction technique is developed as well. Experimental results on synthetic and real-world graphs show that the proposed techniques are well suited to compute BC scores in graphs which are too large to fit in the memory of a single computational node.\",\"PeriodicalId\":226569,\"journal\":{\"name\":\"Proceedings of the ACM International Conference on Computing Frontiers\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM International Conference on Computing Frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2903150.2903153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2903150.2903153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scalable betweenness centrality on multi-GPU systems
Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing techniques to provide results in a reasonable amount of time. Our approach combines bi-dimensional (2-D) decomposition of the graph and multi-level parallelism together with a suitable data-thread mapping that overcomes most of the difficulties caused by the irregularity of the computation on GPUs. In order to reduce time and space requirements of BC computation, a heuristics based on 1-degree reduction technique is developed as well. Experimental results on synthetic and real-world graphs show that the proposed techniques are well suited to compute BC scores in graphs which are too large to fit in the memory of a single computational node.