The Missing Adapter Layer for Research Computing
Authors:
Bowen Li,
Jiazhu Xie,
Chelsea Wang,
Alessandro Umberto D'Aloia,
Ziqi Xu,
Fengling Han
Abstract:
Higher Degree by Research (HDR) candidates increasingly depend on cloud-provisioned virtual machines and local GPU hardware for their computational experiments, yet a persistent and under-addressed gap separates having compute resources from using them productively. Cloud and infrastructure teams can provision a virtual machine in minutes, but the path from a raw VM to a reproducible, GPU-ready re…
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Higher Degree by Research (HDR) candidates increasingly depend on cloud-provisioned virtual machines and local GPU hardware for their computational experiments, yet a persistent and under-addressed gap separates having compute resources from using them productively. Cloud and infrastructure teams can provision a virtual machine in minutes, but the path from a raw VM to a reproducible, GPU-ready research environment remains a significant barrier for researchers who are domain experts, not systems engineers. We argue that this gap is not a shortcoming of any particular tool but a missing architectural layer: an adapter layer that bridges cloud provisioning and interactive research work. We present a lightweight, open-source implementation of this layer, built on k3s and Coder and already in active use in our research workspace environment. A CI/CD pipeline connects GitHub directly to the local cluster, carrying a research project from commit to a running, accessible workspace in under five minutes. We then define a concrete metrics framework for evaluating any adapter layer covering deployment latency, environment reproducibility, onboarding friction, and resource utilisation and establish baselines against which improvements can be measured.
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Submitted 20 July, 2026; v1 submitted 25 March, 2026;
originally announced March 2026.