文章基本信息

标题：Evaluate Metadata of Sparse Matrix for SpMV on Shared Memory Architecture
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作者：Nazmul Ahasan Maruf ; Waseem Ahmed
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
DOI：10.14569/IJACSA.2019.0101182
出版社：Science and Information Society (SAI)
摘要：Sparse Matrix operations are frequently used op-erations in scientific, engineering and high-performance com-puting (HPC) applications. Among them, sparse matrix-vector multiplication (SpMV) is a popular kernel and considered an important numerical method for science, engineering and in scientific computing. However, SpMV is a computationally expen-sive operation. To obtain better performance, SpMV depends on certain factors; choosing the right storage format for the sparse matrix is one of them. Other things like data access pattern, the sparsity of the matrix data set, load balancing, sharing of the memory hierarchy, etc. are other factors that affect performance. Metadata, that describes the substructure of the sparse matrix, like shape, density, sparsity, etc. of the sparse matrix also affects performance efficiency for any sparse matrix operation. Various approaches presented in literature over the last few decades given good results for certain types of matrix structures and don’t perform as well with others. Developers thus are faced with a difficulty in choosing the most appropriate format. In this research, an approach is presented that evaluates metadata of a given sparse matrix and suggest to the developers the most suitable storage format to use for SpMV.
关键词：Sparse matrix vector multiplication; sparse matrix metadata; sparse matrix vector multiplication parallelization; shared memory architecture; sparse matrix storage formats; high performance computing