What Are HPC Workloads in the Cloud?
High-performance computing (HPC) workloads involve computationally intensive tasks that require significant processing power, memory, or specialised hardware. Running these in the cloud provides access to massive compute capacity on-demand without the capital expenditure of building on-premises clusters.
Why Move HPC to the Cloud?
Traditional on-premises HPC clusters involve multi-million pound capital investments, long procurement cycles, and utilisation challenges. Cloud HPC offers elastic scaling, pay-per-use pricing, access to latest GPU and CPU generations, and the ability to burst capacity for peak demand.
How Does Cloud HPC Architecture Differ from Traditional HPC?
Cloud HPC replaces static cluster provisioning with dynamic, elastic architectures. Job schedulers integrate with auto-scaling groups, shared storage uses parallel file systems like Lustre or FSx, and spot/preemptible instances can reduce costs by 60-90% for fault-tolerant workloads.
Architecture Patterns for Cloud HPC
The key patterns include burst-to-cloud (hybrid HPC), fully cloud-native clusters, serverless HPC for embarrassingly parallel workloads, and GPU-accelerated computing for AI training and simulation. Each pattern suits different workload characteristics and cost models.
Cost Optimisation Strategies
Cloud HPC costs can spiral without careful management. Key strategies include spot instance utilisation for fault-tolerant jobs, intelligent job scheduling to maximise utilisation, right-sizing instance families, and implementing automated cluster scaling that responds to queue depth rather than keeping capacity hot.