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dc.contributor.authorCadenelli, Nicola
dc.contributor.authorJaksic, Zoran
dc.contributor.authorPolo Bardés, Jordà
dc.contributor.authorCarrera, David
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2019-07-23T13:09:25Z
dc.date.available2019-07-23T13:09:25Z
dc.date.issued2019-05
dc.identifier.citationCadenelli, N. [et al.]. Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads. "Future Generation Computer Systems", Maig 2019, vol. 94, p. 148-159.
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/2117/166633
dc.description.abstractThe 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.
dc.description.sponsorshipThis 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).
dc.format.extent12 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshHigh performance computing
dc.subject.otherFPGAs
dc.subject.otherGPUs
dc.subject.otherOpenCL
dc.subject.otherGenomics
dc.subject.otherK-mer
dc.subject.otherEnergy-to-solution
dc.titleConsiderations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
dc.typeArticle
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1016/j.future.2018.11.028
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X18314183
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/639595/EU/Holistic Integration of Emerging Supercomputing Technologies/Hi-EST
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/
local.citation.publicationNameFuture Generation Computer Systems
local.citation.volume94
local.citation.startingPage148
local.citation.endingPage159


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