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HPC Extension Strategy for HybridOps.Studio

Status

Proposed — a forward-looking design to extend the HybridOps.Studio control plane toward HPC (High-Performance Computing) workloads and research environments.

Context

HybridOps.Studio currently focuses on hybrid enterprise automation — connecting on-premises clusters, CI/CD pipelines, and public clouds.
However, demand for compute-intensive data analysis, AI model training, and simulation workflows is growing rapidly across enterprise and academic sectors.

To remain future-ready, the platform must support HPC-style workloads (MPI, SLURM, CUDA, large-memory nodes) without compromising its reproducibility and DevOps governance model.

Problem Statement

Traditional HPC systems rely on tightly coupled infrastructure and bespoke schedulers.
HybridOps aims to introduce a DevOps-style abstraction for HPC:
reproducible environment provisioning, controlled scaling, and consistent logging under existing CI/CD governance.

Decision

Introduce a modular HPC extension layer leveraging existing HybridOps primitives.

Design Highlights

  • Scheduler: Integrate SLURM within a dedicated “HPC cluster” namespace.
  • Compute nodes: Provisioned dynamically via Terraform + Ansible on dedicated Proxmox or cloud instances.
  • Workload packaging: Use containerized job runners (Apptainer/Singularity).
  • Networking: High-speed vSwitch or VLAN-backed NICs (SR-IOV optional).
  • Storage: Shared NFS or CephFS mounted under /mnt/hpc-data.
  • Observability: Prometheus HPC exporter integrated into global federation.
  • Governance: Same “Environment Guard” rules applied to HPC pipelines for auditability.

Roadmap Phases

  1. Prototype — deploy single-rack SLURM cluster using HybridOps provisioning (target: Q1 2026).
  2. Integration — add job submission from Jenkins pipelines (evidence-driven).
  3. Federation — connect on-prem HPC to cloud burst nodes (GCP Preemptible or Azure Spot).
  4. Governance — enforce RTO/RPO and audit alignment with existing control plane.

Consequences

  • ✅ Expands HybridOps.Studio use cases into HPC/AI workloads.
  • ✅ Demonstrates infrastructure scalability for enterprise research environments.
  • ✅ Aligns DevOps and scientific computing governance under one platform.
  • ⚠️ Increases complexity — requires new monitoring and cost controls.
  • ⚠️ Not all HPC workloads will suit containerized scheduling initially.

References

Maintainer: HybridOps.Studio License: MIT-0 for code, CC-BY-4.0 for documentation unless otherwise stated.