Accelerating the Explicitly Restarted Arnoldi Method with GPUs Using an Autotuned Matrix Vector Product

DOI10.1137/10079906XzbMath1232.65193OpenAlexW2130748128MaRDI QIDQ3116472

Jérôme Dubois, C. Calvin, Serge G. Petiton

Publication date: 23 February 2012

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/10079906x

zbMATH Keywords

eigenproblemparallel computingmulticoreArnoldi iterationgeneral-purpose graphics processing unit\texttt{ERAM}

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Parallel algorithms in computer science (68W10) Eigenvalues, singular values, and eigenvectors (15A18) Parallel numerical computation (65Y05) Software, source code, etc. for problems pertaining to numerical analysis (65-04)

Related Items (1)

A Task-Scheduling Approach for Efficient Sparse Symmetric Matrix-Vector Multiplication on a GPU

Uses Software

• CUDA
• SparseMatrix
• CUBLAS
• MERAM

This page was built for publication: Accelerating the Explicitly Restarted Arnoldi Method with GPUs Using an Autotuned Matrix Vector Product