Alps, a versatile research infrastructure

2025-07-03

AI Summary

Traditional HPC architectures are vertically integrated and inflexible, struggling to support multidisciplinary, heterogeneous computing workloads. Method: This project introduces Alps—a resource-decoupled HPC infrastructure—featuring the novel software-defined cluster (vCluster) paradigm. vCluster achieves hierarchical decoupling of infrastructure, services, and user environments, enabling seamless integration of HPC and cloud computing paradigms. Leveraging Slingshot high-speed interconnects, Alps integrates heterogeneous CPU/GPU compute resources with modular storage to form a composable, customizable research platform. Contribution/Results: Alps has been successfully deployed for domain-specific applications—including numerical weather prediction and AI—demonstrating significantly improved resource utilization and markedly enhanced adaptability to diverse scientific workloads. It establishes a scalable, highly flexible new paradigm for scientific computing infrastructure.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

The Swiss National Supercomputing Centre (CSCS) has a long-standing tradition of delivering top-tier high-performance computing systems, exemplified by the Piz Daint supercomputer. However, the increasing diversity of scientific needs has exposed limitations in traditional vertically integrated HPC architectures, which often lack flexibility and composability. To address these challenges, CSCS developed Alps, a next-generation HPC infrastructure designed with a transformative principle: resources operate as independent endpoints within a high-speed network. This architecture enables the creation of independent tenant-specific and platform-specific services, tailored to diverse scientific requirements. Alps incorporates heterogeneous hardware, including CPUs and GPUs, interconnected by a high-performance Slingshot network, and offers a modular storage system. A key innovation is the versatile software-defined cluster (vCluster) technology, which bridges cloud and HPC paradigms. By abstracting infrastructure, service management, and user environments into distinct layers, vClusters allow for customized platforms that support diverse workloads. Current platforms on Alps serve various scientific domains, including numerical weather prediction, and AI research.

Problem

• Addressing limitations of traditional HPC architectures
• Enabling flexible composable scientific computing resources
• Supporting diverse workloads across scientific domains

Innovation

• Independent endpoints in high-speed network
• Heterogeneous hardware with Slingshot network
• Software-defined vCluster technology