标题： 演进高性能计算服务以在HPE Cray EX上支持机器学习工作负载 显示英文标题

标题： Evolving HPC services to enable ML workloads on HPE Cray EX

摘要： 阿尔卑斯研究基础设施大规模采用GH200技术，配备10,752块GPU。 访问阿尔卑斯为人工智能（AI）和机器学习（ML）领域的研究人员提供了显著的计算优势。 虽然阿尔卑斯服务于广泛的科学社区，但传统的HPC服务单独来看不足以满足ML社区的动态需求。 本文介绍了将HPC服务功能扩展以更好地支持ML工作负载的初步研究。 我们识别了自阿尔卑斯早期访问阶段（2023年）以来瑞士AI社区所观察到的关键挑战和差距，并提出了几种技术增强方案。 这些包括一个用户环境，旨在促进ML工作负载对HPC的采用，平衡性能与灵活性；一个在开发过程中快速评估ML应用性能的工具；可观测性功能和数据产品，用于检查正在进行的大规模ML工作负载；一个简化分配节点计算就绪性验证的工具；一个服务平面基础设施，用于部署各种类型的工作负载，包括支持和推理服务；以及一个针对ML工作负载特定需求定制的存储基础设施。 这些增强旨在促进ML工作负载在HPC系统上的执行，提高系统的易用性和弹性，并更好地满足ML社区的需求。 我们还讨论了当前在安全方面的做法。 本文最后将这些提议置于我们这类HPC基础设施所服务的社区变化的更广泛背景下。 显示英文摘要

摘要： 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.