构建融合HPC与云技术的AI工厂,打通高性能计算与易用服务的鸿沟。
AI Factories: It's time to rethink the Cloud-HPC divide
- 在超算基础上引入云原生技术,实现高性能与易用性的结合。
- 提出Serverless HPC与高性能云两种融合路径,提升资源利用率。
- 适合关注国产AI基础设施建设与算力平台融合的研究者。
人工智能的战略重要性正推动全球范围内的自主AI计划。各国政府正在建设名为AI工厂(AIF)的专用基础设施,以实现技术自主并保障数字生态系统的资源供给。欧洲的EuroHPC联合体已投入数亿欧元,将多个AI工厂建立在现有高性能计算(HPC)超级计算机之上。然而,尽管HPC系统在原始性能上表现优异,却并非为可用性、可访问性或作为面向公众的AI服务(如推理或代理应用)平台而设计。相比之下,AI从业者习惯使用Kubernetes、对象存储等云原生技术,这些技术在传统HPC环境中难以集成。本文倡导在超级计算机中采用双栈架构:同时整合HPC与云原生技术。目标是通过结合高性能硬件加速与便捷的服务化前端,弥合HPC与云计算之间的鸿沟。这种融合使两者优势相互增强。为此,我们将研究HPC中的云挑战(如无服务器超算)以及云技术面临的HPC挑战(如高性能云)。
原文摘要 · Abstract (English)
The strategic importance of artificial intelligence is driving a global push toward Sovereign AI initiatives. Nationwide governments are increasingly developing dedicated infrastructures, called AI Factories (AIF), to achieve technological autonomy and secure the resources necessary to sustain robust local digital ecosystems. In Europe, the EuroHPC Joint Undertaking is investing hundreds of millions of euros into several AI Factories, built atop existing high-performance computing (HPC) supercomputers. However, while HPC systems excel in raw performance, they are not inherently designed for usability, accessibility, or serving as public-facing platforms for AI services such as inference or agentic applications. In contrast, AI practitioners are accustomed to cloud-native technologies like Kubernetes and object storage, tools that are often difficult to integrate within traditional HPC environments. This article advocates for a dual-stack approach within supercomputers: integrating both HPC and cloud-native technologies. Our goal is to bridge the divide between HPC and cloud computing by combining high performance and hardware acceleration with ease of use and service-oriented front-ends. This convergence allows each paradigm to amplify the other. To this end, we will study the cloud challenges of HPC (Serverless HPC) and the HPC challenges of cloud technologies (High-performance Cloud).
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