Sparse Matrix-vector Multiplication on GPGPU Clusters: A New Storage Format and a Scalable Implementation

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...

Full description

Bibliographic Details

Published in:	2012 26th IEEE International Parallel and Distributed Processing Symposium Workshops pp. 1696 - 1702
Main Authors:	Kreutzer, M., Hager, G., Wellein, G., Fehske, H., Basermann, A., Bishop, A. R.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.05.2012
Subjects:	Bandwidth Computational modeling CUDA Error correction codes GPGPU Instruction sets Kernel Sparse matrices Vectors
ISBN:	1467309745, 9781467309745
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!