{"title":"基于改进节点间通信的多核集群系统Tile QR分解","authors":"Tomohiro Suzuki","doi":"10.1109/IPDPSW.2015.145","DOIUrl":null,"url":null,"abstract":"Tile algorithms for matrix decomposition can generate many fine-grained tasks. Therefore, their suitability for processing with multicourse architecture has attracted much attention from the high-performance computing (HPC) community. Our implementation of tile QR decomposition for a cluster system has dynamic scheduling, OpenMP work- sharing, and other useful features. In this article, we discuss the problems in internodes communications that were present in our previous implementation. The improved implementation has both strong and weak scalability.","PeriodicalId":340697,"journal":{"name":"2015 IEEE International Parallel and Distributed Processing Symposium Workshop","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improved Internode Communication for Tile QR Decomposition for Multicore Cluster Systems\",\"authors\":\"Tomohiro Suzuki\",\"doi\":\"10.1109/IPDPSW.2015.145\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tile algorithms for matrix decomposition can generate many fine-grained tasks. Therefore, their suitability for processing with multicourse architecture has attracted much attention from the high-performance computing (HPC) community. Our implementation of tile QR decomposition for a cluster system has dynamic scheduling, OpenMP work- sharing, and other useful features. In this article, we discuss the problems in internodes communications that were present in our previous implementation. The improved implementation has both strong and weak scalability.\",\"PeriodicalId\":340697,\"journal\":{\"name\":\"2015 IEEE International Parallel and Distributed Processing Symposium Workshop\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Parallel and Distributed Processing Symposium Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPSW.2015.145\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Parallel and Distributed Processing Symposium Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPSW.2015.145","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Internode Communication for Tile QR Decomposition for Multicore Cluster Systems
Tile algorithms for matrix decomposition can generate many fine-grained tasks. Therefore, their suitability for processing with multicourse architecture has attracted much attention from the high-performance computing (HPC) community. Our implementation of tile QR decomposition for a cluster system has dynamic scheduling, OpenMP work- sharing, and other useful features. In this article, we discuss the problems in internodes communications that were present in our previous implementation. The improved implementation has both strong and weak scalability.
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