Alps Research Infrastructure

• Alps Research Infrastructure is a modular, service-oriented high-performance computing facility that supports diverse applications such as AI, ML, and numerical weather prediction.
• It employs a three-tier architecture with dynamic vClusters to provide tailored, reproducible user environments and ensure minimal system downtime.
• Equipped with advanced HPE Cray EX systems and robust observability tools, it optimizes data-intensive workloads and accelerates scientific discovery.

The Alps Research Infrastructure is a state-of-the-art, modular high-performance computing (HPC) facility designed and operated by the Swiss National Supercomputing Centre (CSCS) to support a new generation of computationally intensive scientific applications, including those in AI, ML, numerical weather prediction, and large-scale data processing. Alps departs from traditional monolithic HPC frameworks by emphasizing flexibility, composability, and service-oriented management, enabling tailored platforms that accommodate diverse research needs and rapidly changing technological landscapes (2507.01880, 2507.02404).

1. Architectural Principles and System Design

Alps is architected around the principle that all resources—compute, network, and storage—are implemented as independent endpoints within a high-speed interconnect fabric. This design eschews the classical vertically integrated HPC model in favor of a modular, service-oriented system. Resources are assembled dynamically into tenant- and workflow-specific clusters, known as "vClusters" (Editor's term), that abstract away underlying hardware and infrastructure layers (2507.02404).

A three-tier abstraction underpins Alps:

• Infrastructure as Code provisions hardware and network via automation and resource labeling tools (e.g., Manta, Terraform), supporting diverse processor types and specialized accelerators.
• Service Management orchestrates vServices—batch schedulers, storage managers, container orchestration, among others—via immutable, declarative pipelines, using tools such as Nomad, Kubernetes, and ArgoCD.
• User Environments provide domain-specific interfaces (using tools like uenv, Enroot, Podman) and reproducible runtime environments, supporting researcher-defined workflows and custom software stacks.

This architecture allows:

• The coexistence of multiple, independently upgradable software stacks.
• Minimal system-wide downtimes for maintenance or upgrades.
• Rolling deployments of new services and rapid adaptation to community needs.

2. Technological Innovations and Infrastructure Components

The core of Alps utilizes HPE Cray EX systems, integrating a variety of CPUs and GPUs, including AMD Rome-7742, NVIDIA A100, AMD MI250x, AMD MI300A, and an extensive deployment of NVIDIA Grace-Hopper (GH200) chips. The interconnect relies on the high-bandwidth, low-latency Slingshot-11 network, designed to support efficient communication patterns among heterogeneous node pools (2507.02404).

Alps' storage infrastructure adopts a modular design:

• Parallel Filesystems: A Lustre-based system comprising 100PB HDD and 3PB SSD components.
• Specialized Storage: A 1PB VAST system for high-performance workloads.
• Tiered and Container-Native Storage: Integration of local NVMe, NVMe-over-Fabrics, and data formats such as SquashFS to address ML-specific I/O patterns (2507.01880).

The system supports a measured HPL performance of approximately 434 PFlops on the GH200 nodes, placing Alps seventh in the Top500 list (as of November 2024) (2507.02404).

3. Platforms, User Environments, and Scientific Workloads

Alps hosts multiple independent platforms, each tailored to the needs of specific scientific domains:

• Numerical Weather Prediction (NWP): ICON-22 platform for MeteoSwiss operates on 100+ A100 nodes, maintaining production and R&D vClusters for time-critical weather simulation.
• AI and Machine Learning: The AI–ML platform uses ~1,300 GH200 nodes, offering containerized Python environments with pre-configured frameworks such as PyTorch, and supports workflows from model development to large-scale training and inference.
• Beamline Data Processing and Climate Modeling: Platforms such as PSI’s Merlin7 and EXCLAIM (using kilometer-scale ICON models).
• High-Energy Physics and Astrophysics: Platforms under development for communities including the WLCG and the Cherenkov Telescope Array (2507.02404).

Platform composition, resource assignment, and service configuration are managed through version-controlled declarative files, with environments defined at the platform or job level using standard specifications (e.g., TOML-based Environment Definition Files for ML jobs) (2507.01880).

4. Service Plane, Observability, and Operational Enhancements

To meet the dynamic needs of ML and data-driven science, Alps integrates a suite of operational enhancements:

• Service Plane: A Kubernetes-based hybrid plane (realized with RKE2 and GPU Operators) orchestrates both auxiliary research services (e.g., workflow tracking) and inference workloads. This hybridizes VMs and containerized HPC nodes, employs network segmentation (e.g., Cilium CNI), and facilitates rapid provisioning, monitoring, and scaling of services (2507.01880).
• User-Facing Utilities:
  • GPU Saturation Scorer: Aggregates NVIDIA DCGM metrics (SM Occupancy, Memory Bandwidth Utilization, NVLink) into a single saturation score for rapid assessment of ML workload efficiency.
  • Node Vetting and Early Abort: Pre-execution diagnostics (e.g., GPU temperature, NCCL bandwidth) prevent performance bottlenecks by proactively screening nodes before job launches.
  • Observability Framework: The Extensible Monitoring and Observability Infrastructure (EMOI) collects and visualizes telemetry spanning hardware health, network status, filesystem performance, and custom application metrics, supporting both per-job and global views.
• Container Ecosystem: Researchers define environments via TOML-based Environment Definition Files, allowing for portable, reproducible, and vendor-curated software stacks integrated seamlessly with the batch scheduler (2507.01880).

5. Storage and Data Management Strategies

Alps implements a storage architecture responsive to both traditional HPC and ML-centric workloads:

• Tiered Storage: Fast-access layers leverage local and network-based NVMe for high IOPS demands, with capacity layers provided by parallel filesystems (HDD/SSD) and object storage.
• Data Formats: Container-native formats (e.g., SquashFS) aggregate small files, reducing overhead and improving ingest rates for ML applications.
• Performance Diagnostics: Integration with the observability stack enables correlation of storage performance with computational workload metrics, aiding in performance tuning and troubleshooting (2507.01880).

6. Security, Policy, and Operational Considerations

Security is integral to Alps operations, given its role as a publicly funded HPC facility:

• Image Provenance: Container images are built from trusted sources and automatically scanned for vulnerabilities.
• Network Oversight: Outbound communications are closely monitored, with the ability to revoke resource access in response to suspicious behavior.
• User Education: Regular training and documentation emphasize secure practices and outline risks including model poisoning and dependency confusion.
• Access Policy: Large-scale data crawling is restricted to prevent negative impacts such as content provider blacklisting. All policies are implemented in concert with the technical underpinnings of Alps’ container and service plane architecture (2507.01880).

7. Context, Evolution, and Scientific Impact

The Alps Research Infrastructure reflects a shift in HPC philosophy from monolithic, bespoke systems toward modular, composable frameworks that blend the agility of cloud computing with the high-capacity performance required by modern scientific discovery. This paradigm accommodates a growing diversity of research communities, aligns with international initiatives (e.g., EuroHPC's AI Factories), and enables innovations such as rapid service upgrades, platform-as-a-service models, and community-driven infrastructure evolution (2507.02404, 2507.01880).

A plausible implication is that Alps' vCluster abstraction and tiered service layers provide a template for future computational research infrastructures seeking to integrate heterogeneous resources, robust software-defined management, and responsive operational models for emerging scientific requirements.