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Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations

Filippos Pantekis, Phillip James, Oliver Kullmann Orcid Logo

2022 Tenth International Symposium on Computing and Networking (CANDAR)

Swansea University Authors: Filippos Pantekis, Phillip James, Oliver Kullmann Orcid Logo

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DOI (Published version): 10.1109/candar57322.2022.00029

Abstract

In this paper we present how recent hardware revisions and newly introduced approaches to thread collaboration in NVIDIA GPUs and the CUDA toolkit can be used to design an extensible, scalable GPU-based solver for the N-Queens problem. We discuss various design choices ranging from memory structure,...

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Published in: 2022 Tenth International Symposium on Computing and Networking (CANDAR)
ISBN: 978-1-6654-7531-0 978-1-6654-7530-3
ISSN: 2379-1888 2379-1896
Published: IEEE 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61915
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first_indexed 2022-11-15T01:19:55Z
last_indexed 2023-03-07T04:16:39Z
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spelling v2 61915 2022-11-15 Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations 7e3976bc926b363ee1346c423ba74d11 Filippos Pantekis Filippos Pantekis true false fd3b15ff96c5ea91a100131abac558b6 Phillip James Phillip James true false 2b410f26f9324d6b06c2b98f67362d05 0000-0003-3021-0095 Oliver Kullmann Oliver Kullmann true false 2022-11-15 SCS In this paper we present how recent hardware revisions and newly introduced approaches to thread collaboration in NVIDIA GPUs and the CUDA toolkit can be used to design an extensible, scalable GPU-based solver for the N-Queens problem. We discuss various design choices ranging from memory structure, to low-level optimisations on newer GPU hardware that result in strong performance when solving the N-Queens problem using an optimised solving algorithm that can be applied to other similar in nature problems. Conference Paper/Proceeding/Abstract 2022 Tenth International Symposium on Computing and Networking (CANDAR) IEEE 978-1-6654-7531-0 978-1-6654-7530-3 2379-1888 2379-1896 1 11 2022 2022-11-01 10.1109/candar57322.2022.00029 http://dx.doi.org/10.1109/candar57322.2022.00029 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University EPSRC, EP/S015523/1 2023-06-01T14:56:51.8027184 2022-11-15T01:10:26.2607087 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Filippos Pantekis 1 Phillip James 2 Oliver Kullmann 0000-0003-3021-0095 3 61915__25781__2870ab1974cc46ce84f55404cafa32b7.pdf CANDAR_2022_Regular_Paper_NQueens_FINAL.pdf 2022-11-15T01:17:37.4275148 Output 764699 application/pdf Accepted Manuscript true false
title Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
spellingShingle Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
Filippos Pantekis
Phillip James
Oliver Kullmann
title_short Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
title_full Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
title_fullStr Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
title_full_unstemmed Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
title_sort Scalable N-Queens Solving on GPGPUs via Interwarp Collaborations
author_id_str_mv 7e3976bc926b363ee1346c423ba74d11
fd3b15ff96c5ea91a100131abac558b6
2b410f26f9324d6b06c2b98f67362d05
author_id_fullname_str_mv 7e3976bc926b363ee1346c423ba74d11_***_Filippos Pantekis
fd3b15ff96c5ea91a100131abac558b6_***_Phillip James
2b410f26f9324d6b06c2b98f67362d05_***_Oliver Kullmann
author Filippos Pantekis
Phillip James
Oliver Kullmann
author2 Filippos Pantekis
Phillip James
Oliver Kullmann
format Conference Paper/Proceeding/Abstract
container_title 2022 Tenth International Symposium on Computing and Networking (CANDAR)
publishDate 2022
institution Swansea University
isbn 978-1-6654-7531-0
978-1-6654-7530-3
issn 2379-1888
2379-1896
doi_str_mv 10.1109/candar57322.2022.00029
publisher IEEE
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1109/candar57322.2022.00029
document_store_str 1
active_str 0
description In this paper we present how recent hardware revisions and newly introduced approaches to thread collaboration in NVIDIA GPUs and the CUDA toolkit can be used to design an extensible, scalable GPU-based solver for the N-Queens problem. We discuss various design choices ranging from memory structure, to low-level optimisations on newer GPU hardware that result in strong performance when solving the N-Queens problem using an optimised solving algorithm that can be applied to other similar in nature problems.
published_date 2022-11-01T14:56:50Z
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score 11.035634

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