2012/03/26 by Martin Heller, Heller, Martin, Tomáš Oberhuber +1
Computer Science · #Data Structures and Algorithms (cs.DS) #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Matrix Theory and Algorithms #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC) #cs.DC #cs.DS
paper · pdf · doi:10.48550/arxiv.1203.5737
9 pages, 5 figures, 1 code listing
arxiv created 2012/03/26 · openalex publication_date 2012/03/26 · arxiv updated 2012/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present new adaptive format for storing sparse matrices on GPU. We compare it with several other formats including CUSPARSE which is today probably the best choice for processing of sparse matrices on GPU in CUDA. Contrary to CUSPARSE which works with common CSR format, our new format requires conversion. However, multiplication of sparse-matrix and vector is significantly faster for many atrices. We demonstrate it on set of 1 600 matrices and we show for what types of matrices our format is profitable.