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A Motivating Case Study on Code Variant Selection by Reinforcement Learning

Title data

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Project information

Project title:
Project's official titleProject's id
Optimierungstechniken für explizite Verfahren zur GPU-beschleunigten Lösung von Anfangswertproblemen gewöhnlicher Differenzialgleichungen (OTEGO)KO 2252/3-2

Project financing: Deutsche Forschungsgemeinschaft

Abstract in another language

In this paper, we investigate the applicability of reinforcement learning as a possible approach to select code variants. Our approach is based on the observation that code variants are usually convertible between one another by code transformations. Actor-critic proximal policy optimization is identified as a suitable reinforcement learning algorithm. To study its applicability, a software framework is implemented and used to perform experiments on three different hardware platforms using a class of explicit solution methods for systems of ordinary differential equations as an example application.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science II
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science II > Chair Applied Computer Science II - Univ.-Prof. Dr. Thomas Rauber
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Jul 2022 06:36
Last Modified: 18 Jul 2022 06:36
URI: https://eref.uni-bayreuth.de/id/eprint/70578