Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation
| Autor: | Holger Fehske, Moritz Kreutzer, Gerhard Wellein, Alan R. Bishop, Achim Basermann, Georg Hager |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
FOS: Computer and information sciences
Institut für Simulations- und Softwaretechnik Computer science new storage formats Diagonal Parallel computing Verteilte Systeme und Komponentensoftware Matrix (mathematics) GPGPUs FOS: Mathematics Code (cryptography) Overhead (computing) Computer Science - Performance Computer Science - Numerical Analysis Sparse matrix-vector multiplication Numerical Analysis (math.NA) Performance (cs.PF) Computer Science - Distributed Parallel and Cluster Computing Scalability Memory footprint multi-core processors Computer Science - Mathematical Software Multiplication Distributed Parallel and Cluster Computing (cs.DC) General-purpose computing on graphics processing units Mathematical Software (cs.MS) Parallel sparse matrix-vector multiplication |
| Popis: | 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. In our test scenarios the pJDS format cuts the overall spMVM memory footprint on the GPGPU by up to 70%, and achieves 95% 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. Comment: 10 pages, 5 figures. Added reference to other recent sparse matrix formats |
| Databáze: | OpenAIRE |
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