期刊
出版社
IEEE
DOI: 10.1109/IPDPSW.2012.211
关键词
GPGPU; Sparse matrices; CUDA
资金
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- competence network for scientific high performance computing in Bavaria (KONWIHR) via the project HQS@HPC-II
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia Fermi class of GPGPUs. A new padded jagged diagonals storage (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme while making no assumptions about the matrix structure. In our test scenarios the pJDS format cuts the overall spMVM memory footprint on the GPGPU by up to 70%, and achieves 91% to 130% of the ELLPACK-R performance. Using a suitable performance model we identify performance bottlenecks on the node level that invalidate some types of matrix structures for efficient multi-GPGPU parallelization. For appropriate sparsity patterns we extend previous work on distributed-memory parallel spMVM to demonstrate a scalable hybrid MPI-GPGPU code, achieving efficient overlap of communication and computation.
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