计算机科学与探索2012，Vol.6Issue(2)：118-124,7.DOI:10.3778/j.issn.1673-9418.2012.02.003
大规模稀疏矩阵的主特征向量计算优化方法
Optimization of Parallel Principal Eigenvectors Computing for Large-Scale Sparse Matrixes
摘要
Abstract
The principal eigenvectors computing (PEC) is a paramount operation in engineering and scientific computing. Since the general-purpose computing on graphics processing unit (GPGPU) emerges for the outstanding acceleration factors, PEC implementations on graphics processing unit (GPU) have appeared on the scene. This paper analyzes PEC performance bottleneck from the characteristic of application and GPU architecture, and thereforeproposes a new implementation of PEC based on a new matrix storage format, called GPU-ELL, and an optimized thread mapping strategy of GPU. It evaluates the proposed approach over ATI HD Radeon 5850 GPU, and the experimental results show its good performance with average 200 times acceleration of other existing algorithm on CPU, and 2 times of that on GPU.关键词
图形处理单元通用计算(GPGPU)/主特征向量计算/稀疏矩阵向量乘/线程优化Key words
general-purpose computing on graphics processing unit (GPGPU)/ principal eigenvectors computing (PEC)/ sparse matrix vector (SpMV)/ thread optimization分类
信息技术与安全科学引用本文复制引用
王伟,陈建平,曾国荪,俞莉花,谭一鸣..大规模稀疏矩阵的主特征向量计算优化方法[J].计算机科学与探索,2012,6(2):118-124,7.基金项目
The National Natural Science Foundation of China under Grant Nos.61103068,61174158(国家自然科学基金) （国家自然科学基金）
the Joint Funds of NSFC and Microsoft Asia Research under Grant No.60970155 (NSFC-微软亚洲研究院联合资助项目) （NSFC-微软亚洲研究院联合资助项目）
the Doctoral Fund of Ministry of Education of China under Grant No.20090072110035(教育部博士点基金) （教育部博士点基金）
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110072120017(高等学校博士学科点专项科研基金) （高等学校博士学科点专项科研基金）
the Program of Shanghai Subject Chief Scientist under Grant No.10XD1404400(上海市优秀学科带头人计划项目) （上海市优秀学科带头人计划项目）
the Open Fund of State Key Laboratory of High-End Server&Storage Technology under Grant No.2009HSSA06(高效能服务器和存储技术国家重点实验室开放基金). （高效能服务器和存储技术国家重点实验室开放基金）
