10172

A note on the GPU acceleration of eigenvalue computations

K. Rupp, Ph. Tillet, B. F. Smith, T. Grasser, A. Jungel
Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), 2013
BibTeX

Download Download (PDF)   View View   Source Source   

2033

views

Eigenvalue computations for large sparse matrices such as the Lanczos method are commonly based on Krylov subspace techniques. One of the dominant operations in such algorithms are iterated computations of inner products with the same vector in order to preserve orthogonality of the Krylov basis. These operations can be accelerated by existing BLAS functionality using GPUs. However, this is not fully efficient due to unnecessary memory transfers. We present improved implementations in CUDA and OpenCL, which are now available in ViennaCL, PETSc and SLEPc, and demonstrate an up to two-fold performance gain over existing GPU vendor libraries.
No votes yet.
Please wait...

You must be logged in to post a comment.

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Contact us:

contact@hpgu.org