{"title":"平面图中的并行最短路径查询","authors":"L. Aleksandrov, Guillaume Chapuis, H. Djidjev","doi":"10.1145/2915516.2915518","DOIUrl":null,"url":null,"abstract":"We develop several parallel algorithms for shortest distance queries in planar graphs that use graph partitioning in the preprocessing phase to precompute and store distances between selected pairs of vertices. In the query phase, given a pair of arbitrary vertices v and w, the stored information is used to find the distance between v and w fast. The algorithms are implemented and tested on a high performance cluster with upto 256 16-core CPUs and their performances are analyzed and compared.","PeriodicalId":20568,"journal":{"name":"Proceedings of the ACM Workshop on High Performance Graph Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel Shortest-Path Queries in Planar Graphs\",\"authors\":\"L. Aleksandrov, Guillaume Chapuis, H. Djidjev\",\"doi\":\"10.1145/2915516.2915518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop several parallel algorithms for shortest distance queries in planar graphs that use graph partitioning in the preprocessing phase to precompute and store distances between selected pairs of vertices. In the query phase, given a pair of arbitrary vertices v and w, the stored information is used to find the distance between v and w fast. The algorithms are implemented and tested on a high performance cluster with upto 256 16-core CPUs and their performances are analyzed and compared.\",\"PeriodicalId\":20568,\"journal\":{\"name\":\"Proceedings of the ACM Workshop on High Performance Graph Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM Workshop on High Performance Graph Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2915516.2915518\",\"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 Workshop on High Performance Graph Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2915516.2915518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We develop several parallel algorithms for shortest distance queries in planar graphs that use graph partitioning in the preprocessing phase to precompute and store distances between selected pairs of vertices. In the query phase, given a pair of arbitrary vertices v and w, the stored information is used to find the distance between v and w fast. The algorithms are implemented and tested on a high performance cluster with upto 256 16-core CPUs and their performances are analyzed and compared.
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