GenArchBench: Porting and Optimizing a Genomics Benchmark Suite to Arm-based HPC Processors
| Autor: | López Villellas, Lorién |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Marco Sola, Santiago, Moreto Planas, Miquel, Alastruey Benedé, Jesús |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
parallel computing
Informàtica::Enginyeria del software [Àrees temàtiques de la UPC] computació paral·lela microarquitectura high-performance computing Genomics bioinformatics caracterització de rendiment computació vectorial Genòmica Arm hardware High performance computing vector computing bioinformàtica Càlcul intensiu (Informàtica) performance characterization microarchitecture computació d'altes prestacions |
| Popis: | 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 second position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7g) 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 the majority of genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines, let alone exploit the advantages of the newly introduced Scalable Vector Extensions (SVE). This thesis presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected a set of computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. The porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. All in all, 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. Moreover, 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. In this work, we present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different architectures (i.e., A64FX, Graviton3, Intel Xeon Platinum, and AMD EPYC). Additionally, as proof of the impact of this work, we study the performance improvement in a production-ready genomics pipeline using the GenArchBench optimized kernels. |
| Databáze: | OpenAIRE |
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