Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads

The recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this rea...

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Detalhes bibliográficos
Autores: Cadenelli, Nicola, Jaksic, Zoran, Polo Bardés, Jordà, Carrera, David
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/166633
Acesso em linha:https://hdl.handle.net/2117/166633
https://dx.doi.org/10.1016/j.future.2018.11.028
Access Level:acceso abierto
Palavra-chave:High performance computing
FPGAs
GPUs
OpenCL
Genomics
K-mer
Energy-to-solution
Supercomputadors
Àrees temàtiques de la UPC::Informàtica
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spelling Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloadsCadenelli, NicolaJaksic, ZoranPolo Bardés, JordàCarrera, DavidHigh performance computingFPGAsGPUsOpenCLGenomicsK-merEnergy-to-solutionSupercomputadorsÀrees temàtiques de la UPC::InformàticaThe recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this reasons they have been extended to support different computing architectures, such as GPUs and FPGAs, to leverage the form of parallelism typical of each of such architectures. The paper describes how a genomic workload such as k-mer frequency counting that takes advantage of a GPU can be offloaded to one or even more FPGAs. Moreover, it performs a comprehensive analysis of the FPGA acceleration comparing its performance to a non-accelerated configuration and when using a GPU. Lastly, the paper focuses on how, when using accelerators with a throughput-oriented workload, one should also take into consideration both kernel execution time and how well each accelerator board overlaps kernels and PCIe transferred. Results show that acceleration with two FPGAs can improve both time- and energy-to-solution for the entire accelerated part by a factor of 1.32x. Per contra, acceleration with one GPU delivers an improvement of 1.77x in time-to-solution but of a lower 1.49x in energy-to-solution due to persistently higher power consumption. The paper also evaluates how future FPGA boards with components (i.e., off-chip memory and PCIe) on par with those of the GPU board could provide an energy-efficient alternative to GPUs.This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement s No 639595); the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051; the ICREA, Spain Academia program; and the BSC-CNS Severo Ochoa, Spain program (SEV-2015-0493).Peer ReviewedElsevier20192019-05-0120192019-07-23journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/166633https://dx.doi.org/10.1016/j.future.2018.11.028reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 639595 Holistic Integration of Emerging Supercomputing TechnologiesMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VIIopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1666332026-05-27T15:37:01Z
dc.title.none.fl_str_mv Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
title Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
spellingShingle Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
Cadenelli, Nicola
High performance computing
FPGAs
GPUs
OpenCL
Genomics
K-mer
Energy-to-solution
Supercomputadors
Àrees temàtiques de la UPC::Informàtica
title_short Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
title_full Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
title_fullStr Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
title_full_unstemmed Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
title_sort Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
dc.creator.none.fl_str_mv Cadenelli, Nicola
Jaksic, Zoran
Polo Bardés, Jordà
Carrera, David
author Cadenelli, Nicola
author_facet Cadenelli, Nicola
Jaksic, Zoran
Polo Bardés, Jordà
Carrera, David
author_role author
author2 Jaksic, Zoran
Polo Bardés, Jordà
Carrera, David
author2_role author
author
author
dc.subject.none.fl_str_mv High performance computing
FPGAs
GPUs
OpenCL
Genomics
K-mer
Energy-to-solution
Supercomputadors
Àrees temàtiques de la UPC::Informàtica
topic High performance computing
FPGAs
GPUs
OpenCL
Genomics
K-mer
Energy-to-solution
Supercomputadors
Àrees temàtiques de la UPC::Informàtica
description The recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this reasons they have been extended to support different computing architectures, such as GPUs and FPGAs, to leverage the form of parallelism typical of each of such architectures. The paper describes how a genomic workload such as k-mer frequency counting that takes advantage of a GPU can be offloaded to one or even more FPGAs. Moreover, it performs a comprehensive analysis of the FPGA acceleration comparing its performance to a non-accelerated configuration and when using a GPU. Lastly, the paper focuses on how, when using accelerators with a throughput-oriented workload, one should also take into consideration both kernel execution time and how well each accelerator board overlaps kernels and PCIe transferred. Results show that acceleration with two FPGAs can improve both time- and energy-to-solution for the entire accelerated part by a factor of 1.32x. Per contra, acceleration with one GPU delivers an improvement of 1.77x in time-to-solution but of a lower 1.49x in energy-to-solution due to persistently higher power consumption. The paper also evaluates how future FPGA boards with components (i.e., off-chip memory and PCIe) on par with those of the GPU board could provide an energy-efficient alternative to GPUs.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-05-01
2019
2019-07-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/166633
https://dx.doi.org/10.1016/j.future.2018.11.028
url https://hdl.handle.net/2117/166633
https://dx.doi.org/10.1016/j.future.2018.11.028
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 639595 Holistic Integration of Emerging Supercomputing Technologies
Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VII
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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