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基于FPGA稀疏矩阵分解实现.pdf

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基于FPGA稀疏矩阵分解实现.pdf

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基于FPGA稀疏矩阵分解实现.pdf

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文档介绍:Abstract Abstract Sparse matrix operations are widely used inscience and technology,such work puter-aided design,power system optimization design, mathematical programming,structuralanalysis,solving differentialequation,theoretical ics,graph theory matrixalgorithm has been allimportant branch putational sparse linear equation is the core ofmany engineering and study ofsparsematrixoperations isofgreat significance,in which puters anddistributed shared-memory puters take ,with continuous developments inthe silicon industry and advances inthe structural design,FPGA technologies have enabled the implementation of system。on。a-programmable—puting generation ofFPGA hasmade itpossible tointegrate alarge number putation modules and build parallel machines in asingle chip,which improvesperformance significantly putation—intensive dissertation mainly discusses the realization ofsparsematrixfactorizationbased on FPGA. Firstly,the characteristies and research value we describe various methods thathave been explored in thepast forspeeding-up the process ofLU factorization,on the basis ofwhich our designs alepresented including sparse ordering methods,LU factorization,symbolic LUfactorization,numerical LU factorization and theirrealization based on ,by taking advantage of FPGA resources we implement aparallel LU factorization which iSverified by BDB sparse matrixand weanalyze theexperimental results. Key words:Sparse matrix FPGA LU position Parallel LU factorization 西安电子科技大学学位论文独创性(或创新性)声明秉承学校严谨的学风和优良的科学道德,本人声明所呈交的论文是我个人在导师指导下进行的研究工作及取得的研究成果。尽我所知,除了文中特别加以标注和致谢中所罗列的内容以外,论文中不包含其他人已经发表或撰写过的研究成果;也不包含为获得西安电子科技大学或其它教育机构的学位或证书而使用过的材料。与我一同工作的同志对本研究所做的任何贡献均已在论文中做了明确的说明并表示了谢意。申请学位论文与资料若有不实之处,本人承担一切的法律责任。本人签名: 日期望乞至:丝西安电子科技大学关于论文使用授权的说明本人完全了解西安电子科技大学有关保留和使用学位论文的规定,即:研究生在校攻读学位期间论文工作的知识产权单位属西安电子科技大学。学校有权保留送交论文的复印件,允许查阅和借阅论文;学校可以公布论文的全部或部分内容,可以允许