Skip to content
printicon
Show report in:

UMINF 17.19

An Auto-Tuning Framework for a NUMA-Aware Hessenberg Reduction Algorithm

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.

Keywords

Auto-tuning, Binning, Hessenberg reduction, Multistage search, NUMA-aware., Search space decomposition, Tuning framework

Authors

Back Edit this report
Entry responsible: Mahmoud Eljammaly

Page Responsible: Frank Drewes
2022-09-30