HPC in the Cloud: A Beginner’s Guide to High‑Performance Computing, Costs, and Tools

High-Performance Computing (HPC) has evolved from being exclusive to national laboratories and research institutions to becoming widely available through cloud services. This guide aims to demystify cloud-based HPC for beginners, providing insights into its core components, benefits, and practical steps to get started. Target audiences, including researchers, data scientists, engineers, and systems administrators evaluating HPC for compute-intensive workloads, will find valuable information here. By the end of this guide, you will be well-equipped to understand HPC basics, choose the right cloud provider, and implement a simple cluster for your projects.

What is HPC?

Definition and Key Characteristics
High-Performance Computing (HPC) refers to computing tasks that require extensive CPU cores or accelerators (GPUs/FPGA), substantial memory, high memory bandwidth, and low-latency interconnects. It is characterized by performance metrics like FLOPS (floating point operations per second), number of cores/nodes, memory bandwidth, and inter-node latency. HPC typically involves tightly-coupled parallel processing (MPI-style message passing), contrasting with high-throughput computing (HTC), which handles many independent jobs simultaneously.

Typical HPC Applications

• Scientific simulations: Computational Fluid Dynamics (CFD), climate modeling, astrophysics
• Molecular dynamics and computational chemistry
• Large model training for machine learning and deep learning
• Financial Monte Carlo simulations and risk analytics
• Rendering and media processing

Why Run HPC in the Cloud?

Benefits

• On-Demand Capacity and Scaling: Quickly scale to hundreds or thousands of nodes without incurring capital expenses.
• Access to Specialized Hardware: Utilize cutting-edge hardware like NVIDIA A100/A10, high-speed NICs, and FPGAs without lengthy procurement processes.
• Managed Services: Cloud providers offer parallel filesystems and lifecycle tools, reducing operational overhead and enabling teams to focus on research or application logic.

Trade-offs and Considerations

• For steady, predictable workloads, owning servers may be a more cost-effective long-term solution.
• Data transfer costs and regulations often make on-premises resources preferable for sensitive datasets.
• If you already have a finely-tuned on-prem network (e.g., InfiniBand), staying local might yield the best latency and throughput.

For insights on comparing on-prem storage and RAID tuning before fully committing to cloud file systems, check out our Storage RAID Configuration Guide and ZFS resources like ZFS Administration and Tuning for Beginners.

Core Components of Cloud HPC

Compute:

• Instance Types: Cloud providers offer various options, from CPU-optimized to GPU-accelerated instances. Select based on workload requirements: MPI workloads usually require many CPU cores and low interconnect latency, while ML training benefits from multi-GPU nodes.
• Key Specs to Compare: Consider vCPUs/cores, RAM per core, clock speed, GPU model, and support for RDMA/InfiniBand.

Storage:

• Parallel Filesystems: Technologies like Lustre and IBM Spectrum Scale deliver high throughput for shared datasets.
• Object Storage: Ideal for long-term archival (e.g., S3, Azure Blob), often utilized alongside fast parallel filesystems for active workloads.
• I/O Patterns: Understand your requirements for random versus sequential I/O.

Networking:

• Low Latency & High Bandwidth: Essential for tightly coupled jobs, with options like InfiniBand or RDMA-capable NICs offered by cloud providers.
• Placement Groups: Ensures instances are located closely for predictable network performance.

Job Schedulers and Orchestration:

• Traditional schedulers: Slurm, PBS, and LSF manage job queuing and launches. Learning Slurm essentials is beneficial, as many clouds offer templates.
• Managed Options: Consider AWS ParallelCluster or Azure CycleCloud for simplified cluster lifecycle management.

Major Cloud Providers and Managed HPC Offerings

Other providers like Oracle Cloud, IBM Cloud, and specialized HPC hosts offer unique hardware and services. Consider factoring in available images, ecosystem support, and partnerships when making your decision.
For broader trends across vendors, refer to TOP500 for supercomputing insights.

Getting Started: Practical Steps

1. Define the Workload and Metrics

   • Identify if your task is tightly coupled (MPI) or embarrassingly parallel.
   • Establish metrics for success: time-to-solution, cost per run, or throughput (jobs/hour).

2. Choose Provider and Instance Types

   • Match your workload with appropriate instance families: CPU-optimized for MPI, GPU-accelerated for ML.
   • Test smaller clusters first before scaling.

3. Select Storage and Data Flow

   • Opt for a parallel filesystem (e.g., FSx for Lustre) for active runs while archiving data in object storage.
   • Stage inputs in fast filesystems before job initiation to conserve network resources.

4. Provision a Cluster with a Scheduler

   • Leverage managed tools like AWS ParallelCluster or Azure CycleCloud for rapid cluster provisioning.
   • Optionally use containers with Singularity or Docker for application deployment.

5. Run, Profile, and Optimize

   • Begin with small benchmarks and use profiling tools like nvidia-smi and perf for CPU/GPU utilization assessments.
   • Continuously optimize by iterating on instance type, network placement, and scheduler parameters.

6. Implement Cost Controls and Tear Down

   • Automate shutdown of idle clusters; keep track of costs associated with each resource.
   • Utilize spot/preemptible instances when possible, incorporating fault tolerance features.

First Experiment: Spin up a 2–4 node cluster and run the MPI hello program. Use a managed template from AWS ParallelCluster or Azure CycleCloud.

Common Tools and Frameworks

MPI and Parallel Libraries

• Implementations: Choose from Open MPI or MPICH for message passing across nodes; many HPC tasks rely on MPI.
• Shared Memory Parallelism: OpenMP is often used alongside MPI for hybrid parallelism.

Job Schedulers

• Slurm: The leading open-source scheduler; familiarize yourself with commands like sbatch, srun, squeue.

Containers and Orchestration

• Preferred Frameworks: Singularity/Apptainer integrates well with schedulers in HPC settings; Docker is popular in cloud environments.
• Kubernetes: Useful for deploying containerized HPC workflows but may introduce additional complexity.

Monitoring and Profiling Tools

• Use platforms like CloudWatch (AWS) or Azure Monitor for performance tracking.

Performance and Cost Optimization Tips

• Right-Sizing: Ensure you match the core/memory ratio with your application needs.
• Networking and Placement: Use RDMA-capable instances and cluster placement groups to achieve optimal low-latency performance.
• Storage Management: Optimize your filesystem and tune parameters according to the workload requirements.
• Cost-Saving Strategies: Employ spot instances judiciously and automate start/stop schedules for development clusters.

Security, Governance, and Compliance Basics

Access Controls

• Establish least privilege with IAM roles and RBAC; isolate clusters within secure environments.
• Maintain strong data protection by encrypting sensitive information and being aware of regulatory obligations.

Common HPC Use Cases and Example Workflows

Scientific Simulation (CFD)

• Typical workflow: preprocess mesh, stage inputs, launch MPI solver, checkpoint results, and post-process outputs.

Machine Learning Training

• Optimize multi-GPU use with NVLink, caching frequently accessed datasets locally.

Migration and Hybrid Strategies

• Lift-and-Shift vs. Refactor: Quick lift-and-shift can be feasible but might lack efficiency; refactoring provides greater benefits long-term.
• Hybrid Models: Scale on-prem resources during baseline usage and leverage the cloud for peaks, ensuring synchronized environments are maintained.

For building or comparing on-prem hardware, refer to our guides on server hardware configuration and iSCSI vs NFS vs SMB comparisons.

Quick Checklist & Next Steps

Before You Start

• Clearly define your workload type and desired metrics.
• Select a suitable cloud provider and minimal cluster for initial experiments.

On First Run

• Use managed templates to facilitate quick setup; run basic benchmark tests to ascertain performance and cost.
• Implement monitoring and tagging for effective management.

Glossary

• MPI: Message Passing Interface standard for inter-node communication.
• Lustre: Parallel filesystem designed for HPC efficiency.
• EFA: AWS Elastic Fabric Adapter for low-latency network capabilities.
• Preemptible Instances: Cost-effective, short-term usage options.

Conclusion

Start small, measure, and iterate your HPC configurations in the cloud. Utilize managed tools like AWS ParallelCluster and Azure CycleCloud to streamline operations and reduce overhead. As you gain experience, you’ll discover the right balance between cost, performance, and administrative complexity.

References and Further Reading

• AWS High Performance Computing
• Azure High Performance Compute Guidance
• TOP500 Supercomputing Trends
• Building a Home Lab and Hardware Requirements
• Server Hardware Configuration Guide
• Storage RAID Configuration Guide
• Linux Security Hardening
• Windows Automation with PowerShell