HPC & Compute-Intensive Workloads for UK Businesses

The Challenge

Compute-intensive workloads demand enormous processing power that on-premises clusters struggle to provide cost-effectively. Hardware refresh cycles take months, capacity is fixed, and peak demand events leave jobs queued for days.

Organisations in research, financial services, engineering, and data science need burst capacity that scales to thousands of cores. Maintaining dedicated infrastructure for peak demand means paying for idle resources during normal operations.

The complexity of parallel computing, job scheduling, and high-performance storage compounds the challenge. Teams need specialist expertise to exploit cloud HPC services effectively.

Our Approach

Cloudshire’s HPC specialists design cloud architectures that deliver supercomputer-class performance without supercomputer-class costs. We combine deep understanding of parallel computing with cloud infrastructure expertise.

Our approach starts with workload profiling to understand your compute patterns, data dependencies, and performance requirements. We then design architectures that match cloud services to workload characteristics for optimal cost-performance balance.

We leverage managed HPC services, spot instances, and intelligent scheduling to minimise costs while meeting performance targets. Every design includes scaling policies and cost guardrails.

The Process

Our structured engagement moves from workload understanding to optimised cloud execution.

Step 1 — Workload Analysis. We profile your compute workloads to understand CPU/GPU requirements, memory patterns, I/O characteristics, parallelism potential, and scheduling constraints.

Step 2 — Architecture Design. We design HPC architectures using services like AWS Batch, Azure CycleCloud, or GCP HPC Toolkit combined with appropriate instance types, storage, and networking.

Step 3 — Implementation & Migration. We build the cloud HPC environment, migrate workloads, configure job schedulers, and validate results against on-premises baselines.

Step 4 — Optimisation & Scaling. We tune instance selection, implement spot interruption handling, configure auto-scaling policies, and establish FinOps practices for ongoing cost efficiency.

Outcomes You Can Expect

Clients achieve 60-80% cost reduction compared to maintaining on-premises HPC infrastructure. Elastic scaling means capacity grows from zero to thousands of cores in minutes rather than weeks of procurement.

Time-to-results improves dramatically as jobs no longer queue behind limited capacity. Research teams and data scientists gain self-service access to compute resources with appropriate governance and cost controls.

Frequently Asked Questions

What is cloud-based high performance computing?

Cloud-based HPC uses the elastic compute resources of cloud providers to run intensive workloads. It provides access to CPUs, GPUs, high-bandwidth networking, and parallel file systems on demand, eliminating the need for dedicated on-premises clusters.

How does Cloudshire optimise HPC costs?

We use a combination of spot/preemptible instances for fault-tolerant workloads, reserved capacity for baseline demand, auto-scaling to eliminate idle resources, and intelligent job scheduling that matches workloads to the most cost-effective instance types.

Do you support GPU workloads and machine learning training?

Yes. We design architectures for GPU-intensive workloads including ML model training, inference, simulation, and rendering. We select optimal GPU instance types and implement distributed training frameworks for large-scale workloads.