{"title":"基于TBB的并行Crout算法","authors":"Liyan Zhang, Yan Sun, Jian Ma","doi":"10.1109/ICSESS.2011.5982298","DOIUrl":null,"url":null,"abstract":"The paper presents a novel Parallel Crout Algorithm (PCA) based on multi-core computer with Threading Building Blocks (TBB). TBB offers a rich and complete approach to express parallelism in a C++ program. PCA is decomposed into three-tier: data decomposition parallelism, task processing parallelism and data composition parallelism and it can improve the efficiency of solving linear systems. Compared with Sequential Crout Algorithm (SCA), PCA has advantages of high efficiency, cross-platform and scalability. SCA and PCA, which is based on TBB, are implemented with C++. The validities of both methods are verified by different scale of matrix. In order to improve decomposition rate, the paper optimizes the parameters of PCA. Experiments show that, compared with SCA, PCA can reached a faster solution speed and a higher efficiency and it takes full advantage of Symmetrical Multi-Processing computer.","PeriodicalId":108533,"journal":{"name":"2011 IEEE 2nd International Conference on Software Engineering and Service Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Parallel Crout Algorithm based on TBB\",\"authors\":\"Liyan Zhang, Yan Sun, Jian Ma\",\"doi\":\"10.1109/ICSESS.2011.5982298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a novel Parallel Crout Algorithm (PCA) based on multi-core computer with Threading Building Blocks (TBB). TBB offers a rich and complete approach to express parallelism in a C++ program. PCA is decomposed into three-tier: data decomposition parallelism, task processing parallelism and data composition parallelism and it can improve the efficiency of solving linear systems. Compared with Sequential Crout Algorithm (SCA), PCA has advantages of high efficiency, cross-platform and scalability. SCA and PCA, which is based on TBB, are implemented with C++. The validities of both methods are verified by different scale of matrix. In order to improve decomposition rate, the paper optimizes the parameters of PCA. Experiments show that, compared with SCA, PCA can reached a faster solution speed and a higher efficiency and it takes full advantage of Symmetrical Multi-Processing computer.\",\"PeriodicalId\":108533,\"journal\":{\"name\":\"2011 IEEE 2nd International Conference on Software Engineering and Service Science\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 2nd International Conference on Software Engineering and Service Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSESS.2011.5982298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 2nd International Conference on Software Engineering and Service Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2011.5982298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper presents a novel Parallel Crout Algorithm (PCA) based on multi-core computer with Threading Building Blocks (TBB). TBB offers a rich and complete approach to express parallelism in a C++ program. PCA is decomposed into three-tier: data decomposition parallelism, task processing parallelism and data composition parallelism and it can improve the efficiency of solving linear systems. Compared with Sequential Crout Algorithm (SCA), PCA has advantages of high efficiency, cross-platform and scalability. SCA and PCA, which is based on TBB, are implemented with C++. The validities of both methods are verified by different scale of matrix. In order to improve decomposition rate, the paper optimizes the parameters of PCA. Experiments show that, compared with SCA, PCA can reached a faster solution speed and a higher efficiency and it takes full advantage of Symmetrical Multi-Processing computer.
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