{"title":"克服不规则稀疏矩阵的负载不平衡","authors":"Goran Flegar, H. Anzt","doi":"10.1145/3149704.3149767","DOIUrl":null,"url":null,"abstract":"In this paper we propose a load-balanced GPU kernel for computing the sparse matrix vector (SpMV) product. Making heavy use of the latest GPU programming features, we also enable satisfying performance for irregular and unbalanced matrices. In a performance comparison using 400 test matrices we reveal the new kernel being superior to the most popular SpMV implementations.","PeriodicalId":292798,"journal":{"name":"Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Overcoming Load Imbalance for Irregular Sparse Matrices\",\"authors\":\"Goran Flegar, H. Anzt\",\"doi\":\"10.1145/3149704.3149767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a load-balanced GPU kernel for computing the sparse matrix vector (SpMV) product. Making heavy use of the latest GPU programming features, we also enable satisfying performance for irregular and unbalanced matrices. In a performance comparison using 400 test matrices we reveal the new kernel being superior to the most popular SpMV implementations.\",\"PeriodicalId\":292798,\"journal\":{\"name\":\"Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3149704.3149767\",\"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 Seventh Workshop on Irregular Applications: Architectures and Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3149704.3149767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Overcoming Load Imbalance for Irregular Sparse Matrices
In this paper we propose a load-balanced GPU kernel for computing the sparse matrix vector (SpMV) product. Making heavy use of the latest GPU programming features, we also enable satisfying performance for irregular and unbalanced matrices. In a performance comparison using 400 test matrices we reveal the new kernel being superior to the most popular SpMV implementations.
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