Adventures with Grace Hopper AI Super Chip and the National Research Platform
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916448701513728 |
|---|---|
| author | Hurt, J. Alex Scott, Grant J. Weitzel, Derek Zhu, Huijun |
| author_facet | Hurt, J. Alex Scott, Grant J. Weitzel, Derek Zhu, Huijun |
| contents | The National Science Foundation (NSF) funded National Research Platform (NRP) is a hyper-converged cluster of nationally and globally interconnected heterogeneous computing resources. The dominant computing environment of the NRP is the x86 64 instruction set architecture (ISA), often with graphics processing units (GPUs). Researchers across the nation leverage containers and Kubernetes to execute high-throughput computing (HTC) workloads across the heterogeneous cyberinfrastructure with minimal friction and maximum flexibility. As part of the NSF-funded GP-ENGINE project, we stood up the first server with an NVIDIA Grace Hopper AI Chip (GH200), an alternative ARM ISA, for the NRP. This presents challenges, as containers must be specifically built for ARM versus x86 64. Herein, we describe the challenges encountered, as well as our resulting solutions and some relevant performance benchmarks. We specifically compare the GH200 to A100 for computer vision workloads, within compute nodes in the NRP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16487 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adventures with Grace Hopper AI Super Chip and the National Research Platform Hurt, J. Alex Scott, Grant J. Weitzel, Derek Zhu, Huijun Distributed, Parallel, and Cluster Computing The National Science Foundation (NSF) funded National Research Platform (NRP) is a hyper-converged cluster of nationally and globally interconnected heterogeneous computing resources. The dominant computing environment of the NRP is the x86 64 instruction set architecture (ISA), often with graphics processing units (GPUs). Researchers across the nation leverage containers and Kubernetes to execute high-throughput computing (HTC) workloads across the heterogeneous cyberinfrastructure with minimal friction and maximum flexibility. As part of the NSF-funded GP-ENGINE project, we stood up the first server with an NVIDIA Grace Hopper AI Chip (GH200), an alternative ARM ISA, for the NRP. This presents challenges, as containers must be specifically built for ARM versus x86 64. Herein, we describe the challenges encountered, as well as our resulting solutions and some relevant performance benchmarks. We specifically compare the GH200 to A100 for computer vision workloads, within compute nodes in the NRP. |
| title | Adventures with Grace Hopper AI Super Chip and the National Research Platform |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.16487 |