000109567 001__ 109567
000109567 005__ 20220208123552.0
000109567 0247_ $$2doi$$a10.1109/TCBB.2018.2884701
000109567 0248_ $$2sideral$$a110870
000109567 037__ $$aART-2020-110870
000109567 041__ $$aeng
000109567 100__ $$aHerruzo, J.M.
000109567 245__ $$aAccelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor
000109567 260__ $$c2020
000109567 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109567 5203_ $$aFM-index is a compact data structure suitable for fast matches of short reads to large reference genomes. The matching algorithm using this index exhibits irregular memory access patterns that cause frequent cache misses, resulting in a memory bound problem. This paper analyzes different FM-index versions presented in the literature, focusing on those computing aspects related to the data access. As a result of the analysis, we propose a new organization of FM-index that minimizes the demand for memory bandwidth, allowing a great improvement of performance on processors with high-bandwidth memory, such as the second-generation Intel Xeon Phi (Knights Landing, or KNL), integrating ultra high-bandwidth stacked memory technology. As the roofline model shows, our implementation reaches 95% of the peak random access bandwidth limit when executed on the KNL and almost all the available bandwidth when executed on other Intel Xeon architectures with conventional DDR memory. In addition, the obtained throughput in KNL is much higher than the results reported for GPUs in the literature. IEEE
000109567 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109567 590__ $$a3.71$$b2020
000109567 591__ $$aSTATISTICS & PROBABILITY$$b15 / 125 = 0.12$$c2020$$dQ1$$eT1
000109567 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b18 / 108 = 0.167$$c2020$$dQ1$$eT1
000109567 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b26 / 77 = 0.338$$c2020$$dQ2$$eT2
000109567 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b46 / 112 = 0.411$$c2020$$dQ2$$eT2
000109567 592__ $$a0.745$$b2020
000109567 593__ $$aApplied Mathematics$$c2020$$dQ2
000109567 593__ $$aGenetics$$c2020$$dQ2
000109567 593__ $$aBiotechnology$$c2020$$dQ2
000109567 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000109567 700__ $$aGonzalez Navarro, S.
000109567 700__ $$0(orcid)0000-0002-5916-7898$$aIbañez, P.$$uUniversidad de Zaragoza
000109567 700__ $$0(orcid)0000-0002-5976-1352$$aViñals Yufera, V.$$uUniversidad de Zaragoza
000109567 700__ $$0(orcid)0000-0003-4164-5078$$aAlastruey, J.$$uUniversidad de Zaragoza
000109567 700__ $$aPlata, O.
000109567 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000109567 773__ $$g17, 4 (2020), 1093-1104$$pIEEE-ACM Trans. Comput. Biol. Bioinform.$$tIEEE-ACM Transactions on Computational Biology and Bioinformatics$$x1545-5963
000109567 8564_ $$s1825895$$uhttps://zaguan.unizar.es/record/109567/files/texto_completo.pdf$$yPostprint
000109567 8564_ $$s3485238$$uhttps://zaguan.unizar.es/record/109567/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000109567 909CO $$ooai:zaguan.unizar.es:109567$$particulos$$pdriver
000109567 951__ $$a2022-02-08-11:18:19
000109567 980__ $$aARTICLE