dc.contributor.author | Holm, Håvard Heitlo | |
dc.contributor.author | Brodtkorb, André R. | |
dc.contributor.author | Sætra, Martin Lilleeng | |
dc.date.accessioned | 2021-02-15T08:25:06Z | |
dc.date.available | 2021-02-15T08:25:06Z | |
dc.date.created | 2020-01-18T07:30:12Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Advances in Parallel Computing. 2020, 36 593-604. | en_US |
dc.identifier.issn | 0927-5452 | |
dc.identifier.uri | https://hdl.handle.net/11250/2727951 | |
dc.description.abstract | In this work, we examine the performance and energy efficiency when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between GPU generations; and between low-end, mid-range and high-end GPUs. Our findings show that for some combinations of GPU and GPU code, there is a significant speedup for CUDA over OpenCL, but that this does not hold in general. Our experiments show that performance in general varies more between different GPUs, than between using CUDA and OpenCL. Finally, we show that tuning for performance is a good way of tuning for energy efficiency. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing using Python | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 593-604 | en_US |
dc.source.volume | 36 | en_US |
dc.source.journal | Advances in Parallel Computing | en_US |
dc.identifier.doi | 10.3233/APC200089 | |
dc.identifier.cristin | 1776313 | |
dc.relation.project | Norges forskningsråd: 250935 | en_US |
dc.relation.project | Norges forskningsråd: 250935 (GPU Ocean) | en_US |
dc.relation.project | Notur/NorStore: NN9550K | en_US |
dc.description.localcode | © 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |