{"title":"一个在常规应用程序中分布式窃取工作的案例","authors":"Brendan Sheridan, Jeremy T. Fineman","doi":"10.1145/2931028.2931035","DOIUrl":null,"url":null,"abstract":"This paper presents a dynamically heterogeneous architecture use-case that is both realistic and favorable for distributed work-stealing in regular parallel applications. Using a straightforward implementation of distributed dense matrix multiplication in X10's Global Load Balancing (GLB) library, we show that moderate differences in node processing power allow work-stealing to significantly outperform a standard static schedule such as SUMMA. It also scales comparably on up to 128 cores.","PeriodicalId":229668,"journal":{"name":"Proceedings of the 6th ACM SIGPLAN Workshop on X10","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A case for distributed work-stealing in regular applications\",\"authors\":\"Brendan Sheridan, Jeremy T. Fineman\",\"doi\":\"10.1145/2931028.2931035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a dynamically heterogeneous architecture use-case that is both realistic and favorable for distributed work-stealing in regular parallel applications. Using a straightforward implementation of distributed dense matrix multiplication in X10's Global Load Balancing (GLB) library, we show that moderate differences in node processing power allow work-stealing to significantly outperform a standard static schedule such as SUMMA. It also scales comparably on up to 128 cores.\",\"PeriodicalId\":229668,\"journal\":{\"name\":\"Proceedings of the 6th ACM SIGPLAN Workshop on X10\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th ACM SIGPLAN Workshop on X10\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2931028.2931035\",\"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 6th ACM SIGPLAN Workshop on X10","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2931028.2931035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A case for distributed work-stealing in regular applications
This paper presents a dynamically heterogeneous architecture use-case that is both realistic and favorable for distributed work-stealing in regular parallel applications. Using a straightforward implementation of distributed dense matrix multiplication in X10's Global Load Balancing (GLB) library, we show that moderate differences in node processing power allow work-stealing to significantly outperform a standard static schedule such as SUMMA. It also scales comparably on up to 128 cores.
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