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

Abstract

The performance of a recently developed Hessenberg reduction algorithm greatly depends on the values chosen for its tunable parameters. The problem is 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. This paper presents work-in-progress.