Author:
Cavalcanti Bueno Andre Luis,de La Rocque Rodriguez Noemi,Dominguez Sotelino Elisa
Abstract
Purpose
The purpose of this work is to present a methodology that harnesses the computational power of multiple graphics processing units (GPUs) and hides the complexities of tuning GPU parameters from the users.
Design/methodology/approach
A methodology for auto-tuning OpenCL configuration parameters has been developed.
Findings
This described process helps simplify coding and generates a significant gain in time for each method execution.
Originality/value
Most authors develop their GPU applications for specific hardware configurations. In this work, a solution is offered to make the developed code portable to any GPU hardware.
Subject
Computational Theory and Mathematics,Computer Science Applications,General Engineering,Software
Reference26 articles.
1. Solving sparse linear systems with sparse backward error;SIAM Journal on Matrix Analysis and Applications,1989
2. Progress in sparse matrix methods for large linear systems on vector supercomputers;International Journal of High Performance Computing Applications,1987
3. Tuning OPenCL applications with the periscope tuning framework,2016
4. From CUDA to OpenCL: towards a performance-portable solution for multi-platform GPU programming;Parallel Computing,2012
5. The multifrontal solution of indefinite sparse symmetric linear;ACM Transactions on Mathematical Software (TOMS),1983
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