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Online-Autotuning in the Presence of Algorithmic Choice

Pfaffe, Philip; Tillmann, Martin; Walter, Sigmar; Tichy, Walter F. ORCID iD icon


In this paper we explore the problem of autotuning the choice of algorithm. For a given task, there may be multiple algorithms available, each of which may contain its own set of tunable parameters and may provide optimal performance under different sets of inputs. Algorithmic choice is a type of tuning parameter which has not been well studied in the history of autotuning. To close this gap, we examine established autotuning techniques with regard to their ability of handling these parameters. We discuss the inadequacy of the state-of-the-art autotuning toolbox in manipulating algorithmic choice parameters and introduce four strategies to tackle this task. We evaluate our strategies in two case studies of online-autotuning scenarios, both with and without additional, numeric tuning parameters. The strategies are able to determine the optimal algorithm, and can even interoperate with the autotuning of the additional parameters.

DOI: 10.1109/IPDPSW.2017.28
Zitationen: 11
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2017
Sprache Englisch
Identifikator ISBN: 978-1-5386-3409-7
KITopen-ID: 1000071645
Erschienen in IPDPSW 2017 : Proceedings of the IEEE 31st International Parallel and Distributed Processing Symposium Workshops, Lake Buena Vista, Florida, USA, 29th May - 2nd June 2017
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1379-1388
Schlagwörter Algorithm design and analysis, Approximation algorithms, Classification algorithms, Genetic algorithms, Optimization, Runtime, Tuning, algorithmic choice, autotuning, nominal parameters, online-autotuning
Nachgewiesen in Dimensions
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