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dc.contributor.authorQureshi, Yasir Mahmood
dc.contributor.authorHerruzo-Ruiz, José Manuel 
dc.contributor.authorZapater, Marina
dc.contributor.authorOlcoz, Katzalin
dc.contributor.authorGonzález-Navarro, Sonia 
dc.contributor.authorÓscar, Plata
dc.contributor.authorAtienza, David
dc.date.accessioned2024-01-26T12:12:35Z
dc.date.available2024-01-26T12:12:35Z
dc.date.issued2021-12
dc.identifier.citationY. M. Qureshi et al., "Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures," in IEEE Transactions on Computers, vol. 70, no. 12, pp. 2218-2233, 1 Dec. 2021, doi: 10.1109/TC.2020.3041402.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/29314
dc.description.abstractNext generation workloads, such as genome sequencing, have an astounding impact in the healthcare sector. Sequence alignment, the first step in genome sequencing, has experienced recent breakthroughs, which resulted in next generation sequencing (NGS). As NGS applications are memory bounded with random memory access patterns, we propose the use of high bandwidth memories like 3D stacked HBM2, instead of traditional DRAMs like DDR4, along with energy efficient compute cores to improve both performance and energy efficiency. Three state-of-the-art NGS applications, Bowtie2, BWA-MEM and HISAT2, are used as case studies to explore and optimize NGS computing architectures. Then, using the gem5-X architectural simulator, we obtain an overall 68% performance improvement and 71% energy savings using HBM2 instead of DDR4. Furthermore, we propose an architecture based on ARMv8 cores and demonstrate that 16 ARMv8 64-bit OoO cores with HBM2 outperforms 32-cores of Intel Xeon Phi Knights Landing (KNL) processor with 3D stacked memory. Moreover, we show that by using frequency scaling we can achieve up to 59% and 61% energy savings for ARM in-order and OoO cores, respectively. Lastly, we show that many ARMv8 in-order cores at 1.5GHz match the performance of fewer OoO cores at 2GHz, while attaining 4.5x energy savings.es_ES
dc.description.sponsorshipGA No. 725657, GA No. 863337, GA No. 801137, GA No. RTI2018-093684-B-I00, S2018/TCS-4423, TIN2016-80920-R, JA2012 P12-TIC-1470, UMA18-FEDERJA-197es_ES
dc.language.isoenges_ES
dc.publisherIEEE Transactions on Computerses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectGenoma humano - Proceso de datoses_ES
dc.subjectBioinformáticaes_ES
dc.subject.otherGenome sequencinges_ES
dc.subject.otherSequence alignmentes_ES
dc.subject.otherNGSes_ES
dc.subject.otherHPCes_ES
dc.subject.otherHBM2es_ES
dc.subject.otherKNLes_ES
dc.subject.otherArchitecture explorationes_ES
dc.subject.otherMany-corees_ES
dc.titleGenome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectureses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1109/TC.2020.3041402
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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