Automatic Tuning of Sparse Matrix-Vector Multiplication for CRS Format on GPUs

Performance of sparse matrix-vector multiplication (SpMV) on GPUs is highly dependent on the structure of the sparse matrix used in the computation, the computing environment, and the selection of certain parameters. In this paper, we show that the performance achieved using kernel SpMV on GPUs for...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Yoshizawa, H., Takahashi, D.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Acceleration CRS CUDA GPGPU Graphics processing units Instruction sets Iterative methods Kernel Sparse matrices SpMV Vectors
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