Speakers
Description
HPC services are increasingly constrained by fixed on-premises capacity, long procurement cycles, and data centre infrastructure limitations. At the University of Cambridge, these pressures are amplified by rapidly evolving AI workloads, where researchers benefit from access to diverse compute resources, both CPU and GPU, often on short timescales. This work presents our approach to extending the University’s on-premises CSD3 HPC service with an on-demand, burstable compute capability in AWS to meet the requirements of these users and UKAEA as one of our major collaborators. The cloud extension is designed as a premium option for researchers when local capacity is saturated, while maintaining a consistent operational model and user experience. Beyond capacity bursting, the same platform enables “test-before-buy” benchmarking, allowing research groups and service owners to evaluate application performance on candidate hardware configurations prior to committing to new on-premises procurements.
We describe the pilot architecture and operational model, including identity integration using existing LDAP-based credentials, POSIX-compatible storage access via FSx with S3-backed data staging, and a Slurm-based workflow aligned with established allocation and accounting practices. Using representative UKAEA workloads examples, we outline early findings on portability, performance, and cost, and discuss lessons for operating a hybrid HPC service sustainably and securely.