计算机科学与探索Issue(11)：973-982,10.DOI:10.3778/j.issn.1673-9418.1305053
高度可伸缩的稀疏矩阵乘法
Highly Scalable Sparse Matrix Multiplication
摘要
Abstract
Matrix multiplication is an important fundamental operation in algebra and graph algorithms. And matrixes are usually highly sparse when coming to massive data processing. MapReduce is a programming model which can process large data sets effectively. This paper focuses on how to deal with massive sparse matrix multiplication on top of MapReduce programming model. Block based matrix multiplication algorithms aren’t optimized for sparse matrix and produce large amount of redundant communication. This paper proposes a new algorithm named CRM (column row multiplication), and compares it with traditional block based matrix algorithms. The experimental results demonstrate that CRM has higher efficiency and scalability, is suitable for operating on MapReduce and out-performs traditional ways considerably.关键词
稀疏矩阵乘法/分布式计算/HadoopKey words
sparse matrix multiplication/distributed computing/Hadoop分类
信息技术与安全科学引用本文复制引用
吴志川,毛琛,韩蕾,陈立军..高度可伸缩的稀疏矩阵乘法[J].计算机科学与探索,2013,(11):973-982,10.基金项目
The National Natural Science Foundation of China under Grant No.61070042(国家自然科学基金) （国家自然科学基金）
