Application Experiences on a GPU-Accelerated Arm-based HPC Testbed
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2022
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_09731 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Application Experiences on a GPU-Accelerated Arm-based HPC Testbed 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 Distributed, Parallel, and Cluster Computing Hardware Architecture 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. |
| title | Application Experiences on a GPU-Accelerated Arm-based HPC Testbed |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture |
| url | https://arxiv.org/abs/2209.09731 |