Title data
Hacker, Oliver ; Korch, Matthias ; Seiferth, Johannes:
A Motivating Case Study on Code Variant Selection by Reinforcement Learning.
In: Varbanescu, Ana-Lucia ; Bhatele, Abhinav ; Luszczek, Piotr ; Marc, Baboulin
(ed.):
High Performance Computing : Proceedings. -
Cham
: Springer
,
2022
. - pp. 293-312
. - (Lecture Notes in Computer Science
; 13289
)
ISBN 978-3-031-07312-0
DOI: https://doi.org/10.1007/978-3-031-07312-0_15
Project information
Project title: |
Project's official title Project'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 Faculties |
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: | 15 May 2023 07:47 |
URI: | https://eref.uni-bayreuth.de/id/eprint/70578 |