Open this publication in new window or tab >>2017 (English)Report (Other academic)
Abstract [en]
The performance of a recently developed Hessenberg reduction algorithm greatly depends on the values chosen for its tunable parameters. The search space is huge combined with other complications makes the problem hard to solve effectively with generic methods and tools. We describe a modular auto-tuning framework in which the underlying optimization algorithm is easy to substitute. The framework exposes sub-problems of standard auto-tuning type for which existing generic methods can be reused. The outputs of concurrently executing sub-tuners are assembled by the framework into a solution to the original problem.
Place, publisher, year, edition, pages
Umeå: Department of computing science, Umeå university, 2017. p. 14
Series
Report / UMINF, ISSN 0348-0542 ; 17.19
Keywords
Auto-tuning, Tuning framework, Binning, Search space decomposition, Multistage search, Hessenberg reduction, NUMA-aware
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-145297 (URN)
2018-02-282018-02-282018-06-09Bibliographically approved