{"title":"在GPU上求解三对角线系统","authors":"B. J. Murphy","doi":"10.1109/HiPC.2013.6799117","DOIUrl":null,"url":null,"abstract":"We implement a parallel tridiagonal solver based on cyclic reduction (CR) for a graphics processing unit (GPU). The bane of such solvers is a low computation to communication ratio. With this our main consideration we focus our effort on lowering communication costs. In so doing we accelerate system solving. Further, in the diagonally dominant case computation is decoupled into independent partitions allowing for efficient processing of larger systems.","PeriodicalId":206307,"journal":{"name":"20th Annual International Conference on High Performance Computing","volume":"3 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Solving tridiagonal systems on a GPU\",\"authors\":\"B. J. Murphy\",\"doi\":\"10.1109/HiPC.2013.6799117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We implement a parallel tridiagonal solver based on cyclic reduction (CR) for a graphics processing unit (GPU). The bane of such solvers is a low computation to communication ratio. With this our main consideration we focus our effort on lowering communication costs. In so doing we accelerate system solving. Further, in the diagonally dominant case computation is decoupled into independent partitions allowing for efficient processing of larger systems.\",\"PeriodicalId\":206307,\"journal\":{\"name\":\"20th Annual International Conference on High Performance Computing\",\"volume\":\"3 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"20th Annual International Conference on High Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HiPC.2013.6799117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"20th Annual International Conference on High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HiPC.2013.6799117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We implement a parallel tridiagonal solver based on cyclic reduction (CR) for a graphics processing unit (GPU). The bane of such solvers is a low computation to communication ratio. With this our main consideration we focus our effort on lowering communication costs. In so doing we accelerate system solving. Further, in the diagonally dominant case computation is decoupled into independent partitions allowing for efficient processing of larger systems.
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