Analysis of Sparse Matrix-Vector Multiplication Using Iterative Method in CUDA

Abstract

Scaling up the sparse matrix-vector multiplication has been at the heart of numerous studies in both academia and industry. The massive parallelism of graphics processing units offers tremendous performance in many high-performance computing applications. In this work, we discuss performance analysis for parallel implementation of sparse matrix-vector multiplication using the conjugate gradient algorithm that are efficiently implemented on the NVIDIA CUDA architecture to exploit the massive compute power of today's GPUs. The results show that in comparison to the parallel CPU implementations, the parallel version of the conjugate gradient algorithm on GPU is in average 30 times faster depending on computational kernels.