134533 20260217205450.0 doi 10.1016/j.future.2024.03.050 sideral 138191 ART-2024-138191 eng López-Villellas, Lorién Universidad de Zaragoza (orcid)0000-0002-1891-4359 GenArchBench: A genomics benchmark suite for arm HPC processors 2024 Access copy available to the general public Unrestricted Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE). This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels. info:eu-repo/grantAgreement/ES/DGA/T58-23R info:eu-repo/grantAgreement/ES/MICINN/PID2019-105660RB-C21 info:eu-repo/grantAgreement/ES/MICINN/PID2019-107255GB-C21-AEI-10.13039/501100011033 info:eu-repo/grantAgreement/ES/MICINN/PID2022-136454NB-C22 info:eu-repo/grantAgreement/ES/MICINN/TED2021-132634A-I00 info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es 6.1 2024 COMPUTER SCIENCE, THEORY & METHODS 15 / 147 = 0.102 2024 Q1 T1 1.551 2024 Computer Networks and Communications 2024 Q1 Software 2024 Q1 Hardware and Architecture 2024 Q1 17.1 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Langarita-Benítez, Rubén Badouh, Asaf Soria-Pardos, Víctor Aguado-Puig, Quim López-Paradís, Guillem Doblas, Max Setoain, Javier Kim, Chulho Ono, Makoto Armejach, Adrià Marco-Sola, Santiago Alastruey-Benedé, Jesús Universidad de Zaragoza (orcid)0000-0003-4164-5078 Ibáñez, Pablo Universidad de Zaragoza (orcid)0000-0002-5916-7898 Moretó, Miquel 5007 035 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Arquit.Tecnología Comput. 157 (2024), 313-329 Future gener. comput. syst. Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications 0167-739X 1833596 http://zaguan.unizar.es/record/134533/files/texto_completo.pdf Versión publicada 2491666 http://zaguan.unizar.es/record/134533/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:134533 articulos driver 2026-02-17-20:18:50 ARTICLE