Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems

Bioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogene...

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Detalhes bibliográficos
Autores: Costanzo, Manuel, Rucci, Enzo, Naiouf, Marcelo, García Sánchez, Carlos, Prieto Matías, Manuel
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositório:Docta Complutense
Idioma:inglês
OAI Identifier:oai:docta.ucm.es:20.500.14352/113792
Acesso em linha:https://hdl.handle.net/20.500.14352/113792
Access Level:Acceso aberto
Palavra-chave:SYCL
oneAPI
GPU
CUDA
SYCLomatic
Bioinformatics
DNA
Protein
Sequence alignment
Ciencias
1203.17 Informática
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spelling Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based SystemsCostanzo, ManuelRucci, EnzoNaiouf, MarceloGarcía Sánchez, CarlosPrieto Matías, ManuelSYCLoneAPIGPUCUDASYCLomaticBioinformaticsDNAProteinSequence alignmentCiencias1203.17 InformáticaBioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos Group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer, that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of the SW# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel’s oneAPI ecosystem. The experimental results show that SW# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on 5 different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes.Universidad Complutense de Madrid20242024-02-1920242024-02-19journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/113792reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1137922026-06-02T12:44:21Z
dc.title.none.fl_str_mv Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
title Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
spellingShingle Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
Costanzo, Manuel
SYCL
oneAPI
GPU
CUDA
SYCLomatic
Bioinformatics
DNA
Protein
Sequence alignment
Ciencias
1203.17 Informática
title_short Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
title_full Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
title_fullStr Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
title_full_unstemmed Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
title_sort Assessing Opportunities of SYCL for Biological Sequence Alignment on GPU-based Systems
dc.creator.none.fl_str_mv Costanzo, Manuel
Rucci, Enzo
Naiouf, Marcelo
García Sánchez, Carlos
Prieto Matías, Manuel
author Costanzo, Manuel
author_facet Costanzo, Manuel
Rucci, Enzo
Naiouf, Marcelo
García Sánchez, Carlos
Prieto Matías, Manuel
author_role author
author2 Rucci, Enzo
Naiouf, Marcelo
García Sánchez, Carlos
Prieto Matías, Manuel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv SYCL
oneAPI
GPU
CUDA
SYCLomatic
Bioinformatics
DNA
Protein
Sequence alignment
Ciencias
1203.17 Informática
topic SYCL
oneAPI
GPU
CUDA
SYCLomatic
Bioinformatics
DNA
Protein
Sequence alignment
Ciencias
1203.17 Informática
description Bioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos Group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer, that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of the SW# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel’s oneAPI ecosystem. The experimental results show that SW# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on 5 different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-02-19
2024
2024-02-19
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/113792
url https://hdl.handle.net/20.500.14352/113792
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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