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

Authors :
Elwasif, Wael
Godoy, William
Hagerty, Nick
Harris, J. Austin
Hernandez, Oscar
Joo, Balint
Kent, Paul
Lebrun-Grandie, Damien
Maccarthy, Elijah
Vergara, Veronica G. Melesse
Messer, Bronson
Miller, Ross
Opal, Sarp
Bastrakov, Sergei
Bussmann, Michael
Debus, Alexander
Steinger, Klaus
Stephan, Jan
Widera, Rene
Bryngelson, Spencer H.
Berre, Henry Le
Radhakrishnan, Anand
Young, Jefferey
Chandrasekaran, Sunita
Ciorba, Florina
Simsek, Osman
Spiga, Kate Clark Filippo
Hammond, Jeff
Hardy, John E. Stone. David
Keller, Sebastian
Trott, Jean-Guillaume Piccinali. Christian
Source :
Proceedings of the HPC Asia 2023 Workshops, pg 35-49
Publication Year :
2022

Abstract

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 built by GIGABYTE, each one equipped with a server-class Arm CPU from Ampere Computing and A100 data center GPU from NVIDIA Corp. The systems are connected together using Infiniband high-bandwidth low-latency 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.

Details

Database :
arXiv
Journal :
Proceedings of the HPC Asia 2023 Workshops, pg 35-49
Publication Type :
Report
Accession number :
edsarx.2209.09731
Document Type :
Working Paper
Full Text :
https://doi.org/10.1145/3581576.3581621