125986 20241125101138.0 doi 10.1109/TCBB.2023.3264514 sideral 133227 ART-2023-133227 eng Langarita, Rubén Porting and optimizing BWA-MEM2 using the Fujitsu A64FX processor 2023 Access copy available to the general public Unrestricted Sequence 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. info:eu-repo/grantAgreement/ES/AEI/PID2019-105660RB-C21-AEI-10.13039-501100011033 info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R info:eu-repo/grantAgreement/EC/H2020/779877 /EU/Mont-Blanc 2020, European scalable, modular and power efficient HPC processor/Mont-Blanc 2020 This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 779877 -Mont-Blanc 2020 info:eu-repo/grantAgreement/ES/MICINN/PID2019-107255GB-C21-AEI-10.13039/501100011033 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.6 2023 STATISTICS & PROBABILITY 11 / 168 = 0.065 2023 Q1 T1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 16 / 135 = 0.119 2023 Q1 T1 BIOCHEMICAL RESEARCH METHODS 25 / 85 = 0.294 2023 Q2 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 57 / 170 = 0.335 2023 Q2 T2 0.794 2023 Applied Mathematics 2023 Q2 Genetics 2023 Q2 Biotechnology 2023 Q2 7.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Armejach, Adrià Ibáñez, Pablo Universidad de Zaragoza (orcid)0000-0002-5916-7898 Alastruey-Benedé, Jesús Universidad de Zaragoza (orcid)0000-0003-4164-5078 Moretó, Miquel 5007 035 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Arquit.Tecnología Comput. 20, 5 (2023), 3139 - 3153 IEEE-ACM Trans. Comput. Biol. Bioinform. IEEEACM Transactions on Computational Biology and Bioinformatics 1545-5963 2391956 http://zaguan.unizar.es/record/125986/files/texto_completo.pdf Postprint 3378221 http://zaguan.unizar.es/record/125986/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:125986 articulos driver 2024-11-22-12:01:43 ARTICLE