Copyrights to these papers may be held by the publishers. The download files are preprints. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Frédéric de Mesmay, Yevgen Voronenko and Markus Püschel (Proc. IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1-10, 2010)
Offline Library Adaptation Using Automatically Generated Heuristics
Preprint (626 KB)
Published paper (link to publisher)
Bibtex

Automatic tuning has emerged as a solution to provide high-performance libraries for fast changing, increasingly complex computer architectures. We distinguish offline adaptation (e.g., in ATLAS) that is performed during installation without the full problem description from online adaptation (e.g., in FFTW) that is performed at run-time. Offline adaptive libraries are simpler to use, but, unfortunately, writing the adaptation heuristics that power them is a daunting task. The overhead of online adaptive libraries, on the other hand, makes them unsuitable for a number of applications. In this paper, we propose to automatically generate heuristics in the form of decision trees using a statistical classifier, effectively converting an online adaptive library into an offline one. As testbed we use Spiral-generated adaptive transform libraries for current multicores with vector extensions. We show that replacing the online search with generated decision trees maintains a performance competitive with vendor libraries while allowing for a simpler interface and reduced computation overhead.

Keywords:
Learn the current Spiral system, Search/Learning for optimization, General size libraries