Compute4PUNCH & Storage4PUNCH: Federated Infrastructure for Particle, Astro-, and Nuclear Physics

1. Introduction & Overview

PUNCH4NFDI (Particles, Universe, NuClei and Hadrons for the National Research Data Infrastructure) is a major German consortium representing approximately 9,000 scientists from particle, astro-, astroparticle, hadron, and nuclear physics. Funded by the DFG (German Research Foundation), its prime goal is to establish a federated, FAIR (Findable, Accessible, Interoperable, Reusable) science data platform. This platform aims to provide unified access to the diverse and heterogeneous compute and storage resources spread across participating institutions, addressing the common challenge of analyzing exponentially growing data volumes with complex algorithms.

The Compute4PUNCH and Storage4PUNCH concepts are the technical pillars designed to federate in-kind contributions of High-Throughput Compute (HTC), High-Performance Compute (HPC), and Cloud resources, as well as storage systems based on technologies like dCache and XRootD.

2. Federated Heterogeneous Compute Infrastructure (Compute4PUNCH)

The Compute4PUNCH concept addresses the challenge of providing seamless access to a patchwork of community-supplied compute resources with different architectures, OSes, software stacks, and authentication systems.

3. Federated Storage Infrastructure (Storage4PUNCH)

Parallel to compute, the storage resources are federated to provide a unified data access layer.

4. Technical Implementation & Prototype Status

The concepts are under active development. Prototypes integrating initial sets of compute and storage resources have been established. The contribution mentions "first experiences with scientific applications being executed on the available prototypes," indicating that early adopter workflows are being tested to validate the architecture and identify practical hurdles. The combined environment is poised to enable researchers to execute resource-demanding analysis tasks across the federated infrastructure.

5. Core Insight & Analyst Perspective

6. Technical Details & Mathematical Framework

The resource scheduling problem in such a federation can be abstracted. Let $R = \{r_1, r_2, ..., r_n\}$ be the set of heterogeneous resources, each with dynamic properties like available cores $C_i(t)$, memory $M_i(t)$, and specialized hardware (e.g., GPUs). Let $J = \{j_1, j_2, ..., j_m\}$ be the set of jobs with requirements $\text{req}(j_k)$.

The meta-scheduler's objective is a mapping function $\mathcal{M}: J \rightarrow R$ that maximizes a utility function $U$, often a weighted sum of efficiency and fairness, while respecting constraints:

$$ \text{Maximize } U = \alpha \cdot \text{Utilization} + \beta \cdot \text{Fairness} - \gamma \cdot \text{Cost}_{\text{data-movement}} $$ $$ \text{subject to: } \forall r_i, \sum_{j_k \in \mathcal{M}^{-1}(r_i)} \text{req}_{\text{cores}}(j_k) \leq C_i(t) $$

The Cost_{data-movement} term is critical in a federated storage environment, penalizing schedules that require moving large datasets across wide-area networks. This makes the problem distinct from classic cluster scheduling.

The token-based AAI can be modeled as a capability-based access control system. A token $\tau$ issued to user $u$ for resource $r$ is a cryptographically signed statement: $\tau = \text{Sign}_{\text{AAI}}(u, r, \text{scope}, \text{expiry})$. This decentralizes authorization decisions to the resource providers, who only need to validate the token signature.

7. Experimental Results & Chart Description

While the PDF does not include specific quantitative results, the stated "first experiences with scientific applications" imply initial integration tests. We can conceptualize the key performance indicators (KPIs) that should be measured:

8. Analysis Framework: Conceptual Workflow Example

Since the PDF does not include code, here is a conceptual YAML-based workflow description that a researcher might use to define an analysis job for the Compute4PUNCH/Storage4PUNCH federation. This highlights the declarative nature of the targeted system.

# punch_analysis_workflow.yaml
workflow:
  name: "punch4nfdi_federated_analysis"
  user: "researcher@uni-example.de"
  aai_token: "${PUNCH_AAI_TOKEN}"  # Injected from environment

compute:
  requirements:
    cores: 8
    memory: "32GB"
    runtime: "48h"
    software_stack: "punchenv/analysis-suite:latest"  # Resolved via CVMFS/Container
    priority: "medium"

storage:
  input_data:
    - protocol: "root"
      path: "root://storage-a.punch.de//experiment/run2023/data_*.root"
      cache_prefetch: true  # Hint to Storage4PUNCH caching layer
  output_data:
    - protocol: "s3"
      endpoint: "https://object-store.punch.de"
      path: "/results/${WORKFLOW_ID}/histograms.root"

execution:
  entry_point: "jupyterlab"  # Optional: Start interactive session
  # OR
  batch_command: "python /analysis/run_full_chain.py --input ${INPUT_PATH} --output ${OUTPUT_PATH}"

provenance:
  log_level: "detailed"
  export_metadata_to: "meta.punch.de/catalog"

This fictional spec shows how a user declares what they need (resources, software, data) without specifying where it runs. The federation's middleware (HTCondor, TARDIS, storage federation) interprets this spec, finds suitable resources, stages data, injects the software environment, and executes the job, reporting logs and output to the specified locations.

9. Future Applications & Development Roadmap

The PUNCH4NFDI infrastructure lays a foundation for several advanced applications:

• Cross-Experiment/Multi-Messenger Astrophysics Analysis: Seamlessly combine data from particle detectors, telescopes, and gravitational wave observatories in a single analysis workflow, leveraging different specialized compute resources (GPU farms for image analysis, HTC for particle event processing).
• AI/ML Model Training at Scale: The federated resource pool can dynamically provision large, ephemeral clusters for training complex models on distributed datasets without centralizing the data, aligning with federated learning paradigms.
• Interactive Data Exploration and Visualization: Coupling the JupyterHub interface with high-performance, GPU-accelerated remote visualization backends for large-scale simulation data.
• Integration with External e-Infrastructures: The overlay architecture is conceptually compatible with connecting to European-scale resources like the European Open Science Cloud (EOSC) or PRACE HPC systems, acting as a German gateway.

Development Roadmap Priorities:

1. Robustness & Productionization: Moving from prototype to a 24/7 reliable service with SLAs.
2. Intelligent Data Placement: Enhancing the meta-scheduler with data locality awareness to minimize $\text{Cost}_{\text{data-movement}}$.
3. Advanced Metadata Catalog: Implementing a powerful, searchable metadata system atop Storage4PUNCH to enable data discovery based on physics properties.
4. Green Computing Metrics: Integrating tools to monitor and optimize for energy efficiency across the federated resources, a growing concern for large-scale computing.

10. References

1. PUNCH4NFDI Consortium. (2024). "PUNCH4NFDI - Particles, Universe, NuClei and Hadrons for the NFDI." Official Website. https://www.punch4nfdi.de/
2. Thain, D., Tannenbaum, T., & Livny, M. (2005). "Distributed computing in practice: the Condor experience." Concurrency and Computation: Practice and Experience, 17(2-4), 323-356. (The foundational HTCondor paper).
3. Blomer, J., et al. (2011). "The CernVM File System: A scalable, read-only, software distribution service." Journal of Physics: Conference Series, 331(5), 052004. (Details on CVMFS).
4. European Commission. (2024). "European Open Science Cloud (EOSC)." https://eosc-portal.eu/ (For comparison on federation challenges at EU scale).
5. Verma, A., et al. (2015). "Large-scale cluster management at Google with Borg." Proceedings of the European Conference on Computer Systems (EuroSys). (Contrasts clean-slate cluster management with federation overlays).
6. CMS Collaboration. (2021). "CMS Computing Operations in the AWS Cloud." EPJ Web of Conferences, 251, 02006. (Example of hybrid cloud/federation model).
7. FAIR Data Principles. (2016). FORCE11. https://www.go-fair.org/fair-principles/ (The guiding principles for the PUNCH data platform).