{"title":"一种基于MPI和CUDA的并行稀疏近似逆预处理算法","authors":"Yizhou Wang, Wenhao Li, Jiaquan Gao","doi":"10.1016/j.tbench.2021.100007","DOIUrl":null,"url":null,"abstract":"<div><p>In this study, we present an efficient parallel sparse approximate inverse (SPAI) preconditioning algorithm based on MPI and CUDA, called HybridSPAI. For HybridSPAI, it optimizes a latest static SPAI preconditioning algorithm, and is extended from one GPU to multiple GPUs in order to process large-scale matrices. We make the following significant contributions: (1) a general parallel framework for optimizing the static SPAI preconditioner based on MPI and CUDA is presented, and (2) for each component of the preconditioner, a decision tree is established to choose the optimal kernel of computing it. Experimental results show that HybridSPAI is effective, and outperforms the popular preconditioning algorithms in two public libraries, and a latest parallel SPAI preconditioning algorithm.</p></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"1 1","pages":"Article 100007"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772485921000077/pdfft?md5=acaf310d54e04f99040f007213bf2d56&pid=1-s2.0-S2772485921000077-main.pdf","citationCount":"2","resultStr":"{\"title\":\"A parallel sparse approximate inverse preconditioning algorithm based on MPI and CUDA\",\"authors\":\"Yizhou Wang, Wenhao Li, Jiaquan Gao\",\"doi\":\"10.1016/j.tbench.2021.100007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this study, we present an efficient parallel sparse approximate inverse (SPAI) preconditioning algorithm based on MPI and CUDA, called HybridSPAI. For HybridSPAI, it optimizes a latest static SPAI preconditioning algorithm, and is extended from one GPU to multiple GPUs in order to process large-scale matrices. We make the following significant contributions: (1) a general parallel framework for optimizing the static SPAI preconditioner based on MPI and CUDA is presented, and (2) for each component of the preconditioner, a decision tree is established to choose the optimal kernel of computing it. Experimental results show that HybridSPAI is effective, and outperforms the popular preconditioning algorithms in two public libraries, and a latest parallel SPAI preconditioning algorithm.</p></div>\",\"PeriodicalId\":100155,\"journal\":{\"name\":\"BenchCouncil Transactions on Benchmarks, Standards and Evaluations\",\"volume\":\"1 1\",\"pages\":\"Article 100007\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772485921000077/pdfft?md5=acaf310d54e04f99040f007213bf2d56&pid=1-s2.0-S2772485921000077-main.pdf\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BenchCouncil Transactions on Benchmarks, Standards and Evaluations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772485921000077\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772485921000077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A parallel sparse approximate inverse preconditioning algorithm based on MPI and CUDA
In this study, we present an efficient parallel sparse approximate inverse (SPAI) preconditioning algorithm based on MPI and CUDA, called HybridSPAI. For HybridSPAI, it optimizes a latest static SPAI preconditioning algorithm, and is extended from one GPU to multiple GPUs in order to process large-scale matrices. We make the following significant contributions: (1) a general parallel framework for optimizing the static SPAI preconditioner based on MPI and CUDA is presented, and (2) for each component of the preconditioner, a decision tree is established to choose the optimal kernel of computing it. Experimental results show that HybridSPAI is effective, and outperforms the popular preconditioning algorithms in two public libraries, and a latest parallel SPAI preconditioning algorithm.
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