{"title":"在并发图处理中驯服不对齐的图遍历(摘要)","authors":"Xizhe Yin, Zhijia Zhao, Rajiv Gupta","doi":"10.1145/3597635.3598028","DOIUrl":null,"url":null,"abstract":"This work introduces Glign, a runtime system that automatically aligns the graph traversals for concurrent queries. Glign introduces three levels of graph traversal alignment for iterative evaluation of concurrent queries. First, it synchronizes the accesses of different queries to the active parts of the graph within each iteration of the evaluation---intra-iteration alignment. On top of that, Glign leverages a key insight regarding the \"heavy iterations\" in query evaluation to achieveinter-iteration alignment andalignment-aware batching. The former aligns the iterations of different queries to increase the graph access sharing, while the latter tries to group queries of better graph access sharing into the same evaluation batch. Together, these alignment techniques can substantially boost the data locality of concurrent query evaluation. Based on our experiments, Glign outperforms the state-of-the-art concurrent graph processing systems Krill and GraphM by 3.6× and 4.7× on average, respectively.","PeriodicalId":185981,"journal":{"name":"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Taming Misaligned Graph Traversals in Concurrent Graph Processing (Abstract)\",\"authors\":\"Xizhe Yin, Zhijia Zhao, Rajiv Gupta\",\"doi\":\"10.1145/3597635.3598028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work introduces Glign, a runtime system that automatically aligns the graph traversals for concurrent queries. Glign introduces three levels of graph traversal alignment for iterative evaluation of concurrent queries. First, it synchronizes the accesses of different queries to the active parts of the graph within each iteration of the evaluation---intra-iteration alignment. On top of that, Glign leverages a key insight regarding the \\\"heavy iterations\\\" in query evaluation to achieveinter-iteration alignment andalignment-aware batching. The former aligns the iterations of different queries to increase the graph access sharing, while the latter tries to group queries of better graph access sharing into the same evaluation batch. Together, these alignment techniques can substantially boost the data locality of concurrent query evaluation. Based on our experiments, Glign outperforms the state-of-the-art concurrent graph processing systems Krill and GraphM by 3.6× and 4.7× on average, respectively.\",\"PeriodicalId\":185981,\"journal\":{\"name\":\"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3597635.3598028\",\"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 2023 ACM Workshop on Highlights of Parallel Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3597635.3598028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Taming Misaligned Graph Traversals in Concurrent Graph Processing (Abstract)
This work introduces Glign, a runtime system that automatically aligns the graph traversals for concurrent queries. Glign introduces three levels of graph traversal alignment for iterative evaluation of concurrent queries. First, it synchronizes the accesses of different queries to the active parts of the graph within each iteration of the evaluation---intra-iteration alignment. On top of that, Glign leverages a key insight regarding the "heavy iterations" in query evaluation to achieveinter-iteration alignment andalignment-aware batching. The former aligns the iterations of different queries to increase the graph access sharing, while the latter tries to group queries of better graph access sharing into the same evaluation batch. Together, these alignment techniques can substantially boost the data locality of concurrent query evaluation. Based on our experiments, Glign outperforms the state-of-the-art concurrent graph processing systems Krill and GraphM by 3.6× and 4.7× on average, respectively.