SPARSE STORAGE RECOMMENDATION SYSTEM FOR SPARSE MATRIX VECTOR MULTIPLICATION ON GPU

Sparse Matrix Vector Multiplication (SpMV) Ax=b is a well-known kernel in science, engineering, and web world. Harnessing large computing capabilities of GPU device, many sparse storage formats have been proposed to optimize performance of SpMV on GPU. Compressed Sparse Row (CSR), ELLPACK (ELL), Hybrid (HYB), and Aligned COO sparse storage formats are known for efficient implementation of SpMV on GPU for wide spectrum of sparse matrix pattern. Researchers have observed that performance of SpMV on GPU for a given matrix A can vary widely depending on sparse storage format used. Hence, it has become a great challenge to choose an appropriate storage format from this collection for a given sparse matrix. To resolve this problem, this paper proposes an algorithm that recommend highly suitable storage format for a given sparse matrix. This system use simple metrics (like row length, number of rows, number of columns and number of non-zero element) of a given sparse matrix to analyse impact of different storage format on performance of SpMV. To demonstrate influence of this algorithm, performance of SpMV and its associated application - Conjugate Gradient Solver (CGS) over various sparse matrix patterns with various sparse formats have been compared