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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01880 |
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| _version_ | 1866911035339833344 |
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| author | Schuppli, Stefano Mohamed, Fawzi Mendonça, Henrique Mujkanovic, Nina Palme, Elia Conciatore, Dino Drescher, Lukas Gila, Miguel Witlox, Pim VandeVondele, Joost Martinasso, Maxime Schulthess, Thomas C. Hoefler, Torsten |
| author_facet | Schuppli, Stefano Mohamed, Fawzi Mendonça, Henrique Mujkanovic, Nina Palme, Elia Conciatore, Dino Drescher, Lukas Gila, Miguel Witlox, Pim VandeVondele, Joost Martinasso, Maxime Schulthess, Thomas C. Hoefler, Torsten |
| contents | The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps serves a broad range of scientific communities, traditional HPC services alone are not sufficient to meet the dynamic needs of the ML community. This paper presents an initial investigation into extending HPC service capabilities to better support ML workloads. We identify key challenges and gaps we have observed since the early-access phase (2023) of Alps by the Swiss AI community and propose several technological enhancements. These include a user environment designed to facilitate the adoption of HPC for ML workloads, balancing performance with flexibility; a utility for rapid performance screening of ML applications during development; observability capabilities and data products for inspecting ongoing large-scale ML workloads; a utility to simplify the vetting of allocated nodes for compute readiness; a service plane infrastructure to deploy various types of workloads, including support and inference services; and a storage infrastructure tailored to the specific needs of ML workloads. These enhancements aim to facilitate the execution of ML workloads on HPC systems, increase system usability and resilience, and better align with the needs of the ML community. We also discuss our current approach to security aspects. This paper concludes by placing these proposals in the broader context of changes in the communities served by HPC infrastructure like ours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evolving HPC services to enable ML workloads on HPE Cray EX Schuppli, Stefano Mohamed, Fawzi Mendonça, Henrique Mujkanovic, Nina Palme, Elia Conciatore, Dino Drescher, Lukas Gila, Miguel Witlox, Pim VandeVondele, Joost Martinasso, Maxime Schulthess, Thomas C. Hoefler, Torsten Distributed, Parallel, and Cluster Computing Machine Learning The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps serves a broad range of scientific communities, traditional HPC services alone are not sufficient to meet the dynamic needs of the ML community. This paper presents an initial investigation into extending HPC service capabilities to better support ML workloads. We identify key challenges and gaps we have observed since the early-access phase (2023) of Alps by the Swiss AI community and propose several technological enhancements. These include a user environment designed to facilitate the adoption of HPC for ML workloads, balancing performance with flexibility; a utility for rapid performance screening of ML applications during development; observability capabilities and data products for inspecting ongoing large-scale ML workloads; a utility to simplify the vetting of allocated nodes for compute readiness; a service plane infrastructure to deploy various types of workloads, including support and inference services; and a storage infrastructure tailored to the specific needs of ML workloads. These enhancements aim to facilitate the execution of ML workloads on HPC systems, increase system usability and resilience, and better align with the needs of the ML community. We also discuss our current approach to security aspects. This paper concludes by placing these proposals in the broader context of changes in the communities served by HPC infrastructure like ours. |
| title | Evolving HPC services to enable ML workloads on HPE Cray EX |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2507.01880 |