Overview

The HPC Job Observability Service is a specialized microservice proof of concept (POC) designed to bring modern observability practices to High Performance Computing (HPC) environments. Traditional HPC workloads often run as “black boxes” from a metrics perspective. This service bridges that gap by integrating directly with the Slurm workload manager to provide real-time tracking of job resource utilization (CPU, Memory, GPU).

The Problem #

In many HPC clusters, it is difficult for administrators and users to understand exactly how resources are being utilized during a job’s execution. Questions like “Is my job actually using the GPU?” or “Did my job fail because it ran out of memory?” are notoriously hard to answer without detailed, time-series metrics. Standard monitoring tools often aggregate at the host level, losing the context of individual jobs.

Solution Overview #

This project provides a complete observability pipeline that links infrastructure metrics directly to specific HPC jobs:

1. Slurm Integration: Uses prolog and epilog scripts to capture job lifecycle events instantly without polling.
2. Resource Collection: Leverages Linux cgroups v2 for accurate CPU and memory tracking, alongside vendor tools for GPU metrics.
3. Metrics Export: Exposes data via a Prometheus-compatible endpoint, making it easy to create granular Grafana dashboards.

System Architecture #

The system follows a clean microservice architecture built with Go, designed for stability and low overhead on compute nodes.

Lifecycle events, data collection, and metrics export #

Microservice Architecture #

Core Components #

• HTTP API (Go): Built with net/http and adhering to OpenAPI specifications (Design-First). It handles lifecycle events and acts as the bridge between raw system data and proper observability standards.
• PostgreSQL Storage: Maintains the canonical state of jobs, their history, and high-resolution audit logs of all state changes.
• Metrics Exporter: A custom Prometheus exporter that maintains real-time gauges for runtime, CPU usage, memory usage, and GPU utilization.

Technical Highlights #

Event-Based Slurm Integration #

Instead of polling Slurm (which can be slow and stressful for the scheduler), the service uses an event-based approach.

• When a job starts, a lightweight prolog script fires a webhook to the service (/v1/events/job-started).
• When it ends, an epilog script does the same (/v1/events/job-finished), capturing the exit code and signal to accurately determine if the job completed successfully, failed, or was cancelled.

Audit Logging & Traceability #

HPC environments frequently require traceability: who changed a job, what changed, and why. To support this, the service stores an audit trail of job lifecycle events and updates.

At a high level, each significant change to a job results in an audit event that captures:

• Change type (create/upsert/update/delete)
• Actor (for example: slurm-prolog, slurm-epilog, collector, api)
• Source system (Slurm vs mock vs manual API)
• Correlation ID to group related operations across the job lifecycle
• Snapshot of the job at the time of change (for debugging and compliance)

This makes it much easier to debug cases like “why did a job become cancelled?” or “when did we start sampling metrics for this job?” without relying on ephemeral scheduler logs.

Granular Metrics #

The service pushes the boundaries of standard monitoring by collecting:

• CPU Usage: Real-time percent utilization per job.
• Memory: RSS and Cache usage extracted directly from cgroups.
• GPU: Utilization metrics for NVIDIA and AMD cards.

This granularity allows for detailed dashboards where users can correlate code execution phases with resource spikes.

API-First Design with OpenAPI Code Generation #

This project follows an API-first workflow: the OpenAPI specification is treated as the source of truth, and Go types + server interfaces are generated from the spec.

In practice, that means the development loop looks like:

1. Update the OpenAPI YAML specification
2. Run code generation (go generate ./...)
3. Implement or update handler logic against generated interfaces

The benefits are substantial for a microservice that needs to stay maintainable:

• Spec-driven development: the contract is clear and reviewable
• Type safety: fewer runtime errors and less hand-written boilerplate
• Faster iteration: adding endpoints and models becomes mostly “edit spec → generate → implement”
• Consistency: request/response structures are enforced across the codebase

Results #

This tool provides a “glass box” view into HPC jobs, enabling:

• Better Debugging: Users can see exactly when and why a job crashed.
• Efficiency: Admins can identify jobs requesting way more resources than they consume.
• Transparency: Real-time dashboards available to both operations teams and end-users.

Grafana Dashboards (Examples) #

Once metrics are exported in Prometheus format, Grafana dashboards can provide an immediate “single pane of glass” for both job-level and node-level resource behavior.

Limitations & Next Steps #

This service is intentionally a proof of concept, focused on validating the architecture and data flow rather than being a hardened, production-complete platform.

There are plenty of directions to take it further: stronger auth and multi-tenancy, deeper scheduler integrations, more robust error handling and backpressure, richer GPU accounting, and more production-grade deployment and operational tooling.

It’s not a perfect complete solution today, but it’s a strong foundation that could be used to develop something even better.

• Source