000125986 001__ 125986 000125986 005__ 20241125101138.0 000125986 0247_ $$2doi$$a10.1109/TCBB.2023.3264514 000125986 0248_ $$2sideral$$a133227 000125986 037__ $$aART-2023-133227 000125986 041__ $$aeng 000125986 100__ $$aLangarita, Rubén 000125986 245__ $$aPorting and optimizing BWA-MEM2 using the Fujitsu A64FX processor 000125986 260__ $$c2023 000125986 5060_ $$aAccess copy available to the general public$$fUnrestricted 000125986 5203_ $$aSequence alignment pipelines for human genomes are an emerging workload that will dominate in the precision medicine field. BWA-MEM2 is a tool widely used in the scientific community to perform read mapping studies. In this paper, we port BWA-MEM2 to the AArch64 architecture using the ARMv8-A specification, and we compare the resulting version against an Intel Skylake system both in performance and in energy-to-solution. The porting effort entails numerous code modifications, since BWA-MEM2 implements certain kernels using x86 64 specific intrinsics, e.g., AVX-512. To adapt this code we use the recently introduced Arm’s Scalable Vector Extensions (SVE). More specifically, we use Fujitsu’s A64FX processor, the first to implement SVE. The A64FX powers the Fugaku Supercomputer that led the Top500 ranking from June 2020 to November 2021. After porting BWA-MEM2 we define and implement a number of optimizations to improve performance in the A64FX target architecture. We show that while the A64FX performance is lower than that of the Skylake system, A64FX delivers 11.6% better energy-to-solution on average. All the code used for this article is available at https://gitlab.bsc.es/rlangari/bwa-a64fx. 000125986 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2019-105660RB-C21-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R$$9info:eu-repo/grantAgreement/EC/H2020/779877 /EU/Mont-Blanc 2020, European scalable, modular and power efficient HPC processor/Mont-Blanc 2020$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 779877 -Mont-Blanc 2020$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-107255GB-C21-AEI-10.13039/501100011033 000125986 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000125986 590__ $$a3.6$$b2023 000125986 592__ $$a0.794$$b2023 000125986 591__ $$aSTATISTICS & PROBABILITY$$b11 / 168 = 0.065$$c2023$$dQ1$$eT1 000125986 593__ $$aApplied Mathematics$$c2023$$dQ2 000125986 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b16 / 135 = 0.119$$c2023$$dQ1$$eT1 000125986 593__ $$aGenetics$$c2023$$dQ2 000125986 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b25 / 85 = 0.294$$c2023$$dQ2$$eT1 000125986 593__ $$aBiotechnology$$c2023$$dQ2 000125986 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b57 / 170 = 0.335$$c2023$$dQ2$$eT2 000125986 594__ $$a7.5$$b2023 000125986 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000125986 700__ $$aArmejach, Adrià 000125986 700__ $$0(orcid)0000-0002-5916-7898$$aIbáñez, Pablo$$uUniversidad de Zaragoza 000125986 700__ $$0(orcid)0000-0003-4164-5078$$aAlastruey-Benedé, Jesús$$uUniversidad de Zaragoza 000125986 700__ $$aMoretó, Miquel 000125986 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput. 000125986 773__ $$g20, 5 (2023), 3139 - 3153$$pIEEE-ACM Trans. Comput. Biol. Bioinform.$$tIEEEACM Transactions on Computational Biology and Bioinformatics$$x1545-5963 000125986 8564_ $$s2391956$$uhttps://zaguan.unizar.es/record/125986/files/texto_completo.pdf$$yPostprint 000125986 8564_ $$s3378221$$uhttps://zaguan.unizar.es/record/125986/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000125986 909CO $$ooai:zaguan.unizar.es:125986$$particulos$$pdriver 000125986 951__ $$a2024-11-22-12:01:43 000125986 980__ $$aARTICLE