[Paper] Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies

Source: arXiv - 2604.15919v1

Overview

The paper introduces continuous benchmarking, an automated pipeline that brings the rigor of continuous integration (CI) to the world of high‑performance scientific computing. By treating benchmarks as first‑class, version‑controlled artifacts, the authors enable researchers and developers to keep their performance measurements up‑to‑date as models and hardware evolve—crucial for fast‑moving fields like neuroscience simulation and AI.

Key Contributions

• Continuous Benchmarking Framework – Extends CI concepts to automatically run, collect, and compare performance data for large‑scale models on ever‑changing HPC systems.
• User‑Agnostic Operations – Benchmarks are defined once and can be executed by any contributor without needing custom scripts or environment tweaks.
• Modular, Extensible Design – Plug‑in architecture lets teams add new models, metrics, or hardware back‑ends with minimal friction.
• Reproducibility & Result Re‑use – All benchmark runs are versioned, stored, and searchable, allowing past results to be replayed or compared against new code/hardware.
• Open‑Source Toolchain – The authors release the pipeline as a set of reusable components, encouraging community adoption and collaboration.

Methodology

The authors built the pipeline on top of standard CI tools (e.g., GitLab CI, Jenkins) and container technologies (Docker, Singularity) to encapsulate the execution environment. A benchmark definition consists of:

1. Model Specification – A description of the scientific model (e.g., a spiking neural network) and its input parameters.
2. Performance Metrics – Runtime, memory footprint, scaling efficiency, energy consumption, etc.
3. Execution Script – A lightweight wrapper that launches the model on the target HPC system.

When a change is pushed to the repository (new model version, compiler update, hardware driver change), the CI server automatically spins up the appropriate container, schedules the job on the target cluster, runs the benchmark, and pushes the results to a central database. The database is queryable via a web UI and an API, enabling dashboards that show trends over time and across hardware generations.

Results & Findings

• Speed of Feedback – Benchmark cycles that previously took weeks (manual submission, data collection) were reduced to under an hour with the automated pipeline.
• Detecting Regressions – The system caught performance regressions in a popular neural simulation framework after a compiler upgrade, prompting a quick rollback.
• Cross‑Platform Comparisons – By normalizing metrics across different HPC architectures (CPU‑only, GPU‑accelerated, ARM‑based), the authors demonstrated clear trade‑offs for specific model sizes, guiding hardware procurement decisions.
• Community Adoption – Over six months, three external research groups contributed benchmark definitions for their own models, illustrating the framework’s extensibility.

Practical Implications

• For Developers – Integrating continuous benchmarking into your CI pipeline means you’ll know instantly if a code change hurts performance, allowing you to maintain high‑throughput simulations without manual profiling.
• For HPC Operators – The collected benchmark data can inform scheduling policies, capacity planning, and hardware upgrades by showing real‑world workloads rather than synthetic micro‑benchmarks.
• For Researchers – Reproducible, versioned benchmark records make it easier to publish performance claims, compare against prior work, and satisfy reviewer demands for transparency.
• For AI & Neuroscience Projects – As models scale from millions to billions of neurons or parameters, the framework provides a scalable way to track how algorithmic tweaks (e.g., new plasticity rule) interact with hardware trends (e.g., emerging GPU architectures).

Limitations & Future Work

• Hardware Diversity – The current implementation focuses on a handful of HPC clusters; extending to cloud‑based or edge devices will require additional adapters.
• Metric Overhead – Instrumentation (especially energy measurement) can introduce slight perturbations; the authors note the need for more lightweight profiling hooks.
• User Onboarding – While the pipeline is modular, setting up the initial CI environment still demands some DevOps expertise, which may be a barrier for smaller labs.
• Future Directions – Planned work includes automated anomaly detection on benchmark trends, tighter integration with container orchestration platforms (Kubernetes), and support for multi‑objective optimization (e.g., balancing speed vs. energy).

Authors

• Jan Vogelsang
• Melissa Lober
• Catherine Mia Schöfmann
• José Villamar
• Dennis Terhorst
• Johanna Senk
• Hans Ekkehard Plesser
• Markus Diesmann
• Susanne Kunkel
• Anno C. Kurth

Paper Information

• arXiv ID: 2604.15919v1
• Categories: cs.DC
• Published: April 17, 2026
• PDF: Download PDF