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
页码：614-623
出版社：Science and Information Society (SAI)
摘要：Sparse Matrix operations are frequently used operations
in scientific, engineering and high-performance computing
(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 expensive
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.
