{"title":"使用以图为中心的方法进行平行三角形计数和k-桁架识别","authors":"C. Voegele, Yi-Shan Lu, Sreepathi Pai, K. Pingali","doi":"10.1109/HPEC.2017.8091037","DOIUrl":null,"url":null,"abstract":"We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems. Both systems are based on a graph-centric abstraction called the operator formulation of algorithms. Depending on the input graph, our implementations are two to three orders of magnitude faster than the reference implementations provided by the IEEE HPEC static graph challenge.","PeriodicalId":364903,"journal":{"name":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Parallel triangle counting and k-truss identification using graph-centric methods\",\"authors\":\"C. Voegele, Yi-Shan Lu, Sreepathi Pai, K. Pingali\",\"doi\":\"10.1109/HPEC.2017.8091037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems. Both systems are based on a graph-centric abstraction called the operator formulation of algorithms. Depending on the input graph, our implementations are two to three orders of magnitude faster than the reference implementations provided by the IEEE HPEC static graph challenge.\",\"PeriodicalId\":364903,\"journal\":{\"name\":\"2017 IEEE High Performance Extreme Computing Conference (HPEC)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE High Performance Extreme Computing Conference (HPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPEC.2017.8091037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPEC.2017.8091037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel triangle counting and k-truss identification using graph-centric methods
We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems. Both systems are based on a graph-centric abstraction called the operator formulation of algorithms. Depending on the input graph, our implementations are two to three orders of magnitude faster than the reference implementations provided by the IEEE HPEC static graph challenge.
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