Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

作者：Wael Elwasif, William F. Godoy, Nick Hagerty, J. Austin Harris, Óscar Hernández, Bálint Joó, Paul R. C. Kent, Damien Lebrun-Grandié, Elijah MacCarthy, Verónica Melesse Vergara, Bronson Messer, Ross Miller, Sarp Oral, Sergei Bastrakov, Michael Bußmann, Alexander Debus, Klaus Steiniger, Jan Stephan, René Widera, Spencer H. Bryngelson, Henry Le Berre, Anand Radhakrishnan, Jeffrey Young, Sunita Chandrasekaran, Florina M. Ciorba, Osman Seckin Simsek, M. A. Clark, Filippo Spiga, Jeff R. Hammond, John E. Stone, David J. Hardy, Sebastian Keller, Jean-Guillaume Piccinali, Christian Robert Trott · 年份：2023 · DOI：10.1145/3581576.3581621 · 被引用次数：8 · 研究领域：Parallel Computing and Optimization Techniques、Distributed and Parallel Computing Systems、Advanced Data Storage Technologies

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.