An Alternative Compressed Storage Format for Sparse Matrices

Abstract

The handling of the sparse matrix vector product(SMVP) is a common kernel in many scientific applications. This kernel is an irregular problem, which has led to the development of several compressed storage formats such as CRS, CCS, and JDS among others. We propose an alternative storage format, the Transpose Jagged Diagonal Storage(TJDS), which is inspired from the Jagged Diagonal Storage format and makes no assumptions about the sparsity pattern of the matrix. We present a selection of sparse matrices and compare the storage requirements needed using JDS and TJDS formats, and we show that the TDJS format needs less storage space than the JDS format because the permutation array is not required. Another advantage of the proposed format is that although TJDS also suffers the drawback of indirect addressing, it does not need the permutation step after the computation of the SMVP.