Achieving Portable High Performance for Iterative Solvers on Accelerators | Zendy

Rupp Karl | Zendy; Tillet Philippe | Zendy; Jüngel Ansgar | Zendy; Grasser Tibor | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Achieving Portable High Performance for Iterative Solvers on Accelerators

Author(s) -

Rupp Karl,

Tillet Philippe,

Jüngel Ansgar,

Grasser Tibor

Publication year - 2014

Publication title -

pamm

Language(s) - English

Resource type - Journals

ISSN - 1617-7061

DOI - 10.1002/pamm.201410462

Subject(s) - computer science , parallel computing , kernel (algebra) , conjugate gradient method , graphics , residual , vendor , implementation , computational science , minification , computer engineering , algorithm , computer graphics (images) , mathematics , programming language , combinatorics , marketing , business

We propose performance enhancements for the implementation of the conjugate gradient method and the generalized minimum residual method for accelerators such as graphics processing units. Through a minimization of memory transfers from global memory via pipelining as well as a reduction of the number of compute kernels through kernel fusion, the performance is improved by up to two‐fold when compared to standard implementations based on vendor‐tuned routines. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research