Abstract
This paper addresses the performance analysis of sparse matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. Quadro K4200 is the GPU that is used in this paper. On the implementation of matrix-vector multiplication, various sizes and types of matrices are attempted. Our results show that on the average scenarios with 2 partitions, 4 partitions, 8 partitions, 16 partitions and 32 partitions in 1024 threads, CUDA performs up to 700 × better than sequential programming.
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