AWS HPC Services Overview
AWS offers a robust suite of High-Performance Computing (HPC) services, enabling organisations to run large-scale simulations and deep learning workloads in the cloud. With virtually limitless compute capacity, a high-performance file system, and high-throughput networking, AWS HPC services facilitate faster insights and reduced time-to-market.Key AWS HPC services include:
- Amazon Elastic Compute Cloud (EC2): Provides secure, resizable compute capacity for a variety of workloads.
- Elastic Fabric Adapter (EFA): Enables scaling of HPC applications across numerous CPUs and GPUs, offering low-latency, low-jitter channels for inter-instance communications.
- AWS ParallelCluster: An open-source tool that simplifies the deployment and management of HPC clusters.
- AWS Batch: A cloud-native batch scheduler that scales hundreds of thousands of computing jobs across all AWS compute services.
- Amazon FSx for Lustre: A high-performance file system designed for processing massive datasets on-demand with sub-millisecond latencies.
These services, combined with AWS’s fast networking capabilities, empower organisations to accelerate innovation, maximise operational efficiency, and optimise performance.
Essential Best Practices for HPC on AWS
When designing, deploying, and optimising HPC workloads on AWS, consider the following best practices:
- Architectural Design: Choose the right combination of AWS services based on your workload characteristics. For compute-intensive workloads, Amazon EC2 is ideal, while AWS Batch suits batch processing jobs.
- Workload Distribution: Distribute workloads across multiple AWS services to maximise resource utilisation and performance. Use AWS ParallelCluster for managing HPC clusters and AWS Batch for job distribution.
- Performance Optimisation: Select appropriate instance types, employ Elastic Fabric Adapter for low-latency communications, and leverage Amazon FSx for Lustre for high-performance file systems.
- Cost Optimisation: Use Spot Instances for non-time-sensitive workloads to reduce costs. Spot Instances can be integrated into AWS Batch and AWS ParallelCluster to run HPC workloads at lower costs. Consider Savings Plans or Reserved Instances for consistent workloads.
- Security and Compliance: Implement robust access controls using AWS Identity and Access Management (IAM), protect data in transit and at rest with encryption, and ensure compliance with industry standards.
- Scalability: Design your HPC workloads for scalability. Use AWS Auto Scaling to automatically adjust capacity for steady, predictable performance at minimal cost.
- Resilience: Implement fault tolerance and high availability in your HPC architecture. Use multiple Availability Zones to maintain application availability even in the event of a data centre failure.
- Monitoring and Logging: Employ AWS CloudWatch to collect and track metrics, monitor log files, and set alarms. AWS CloudTrail provides event history of AWS account activity for governance and compliance. For HPC workloads, track metrics like CPU and GPU utilisation, network throughput, and storage I/O operations.
- Automation: Automate infrastructure as much as possible. Use AWS CloudFormation or Terraform for infrastructure as code, and automate deployments with AWS CodePipeline and AWS CodeDeploy.
- Data Management: Use suitable storage solutions for your data. Amazon S3 is great for object storage, Amazon EBS for block storage, and Amazon FSx for Lustre for high-performance file systems. For HPC workloads, consider Amazon FSx for Lustre, particularly for applications requiring fast storage and non-sequential read/write access.
AWS HPC Scenarios and Reference Architectures
AWS HPC solutions can be deployed in various scenarios, each with its unique architecture. Here are some key scenarios:
1. Traditional Cluster Environment
Many users start their cloud journey with an environment resembling traditional HPC setups, often involving a login node with a scheduler to launch jobs. AWS ParallelCluster exemplifies an end-to-end cluster provisioning capability based on AWS CloudFormation. It provides an HPC environment that mimics conventional HPC clusters while offering scalability. Traditional cluster architectures can support both loosely and tightly coupled workloads. For optimal performance, tightly coupled workloads should use a compute fleet in a clustered placement group with homogeneous instance types.

2. Batch-Based Architecture
AWS Batch is a fully managed service that allows you to run large-scale compute workloads in the cloud without the need to provision resources or manage schedulers. It dynamically provisions the optimal quantity and type of compute resources based on the volume and specified resource requirements of the submitted batch jobs. An AWS Batch-based architecture can accommodate both loosely and tightly coupled workloads, with tightly coupled workloads utilizing Multi-node Parallel Jobs in AWS Batch.

3. Queue-Based Architecture
Amazon SQS is a fully managed message queuing service that simplifies the decoupling of pre-processing, compute, and post-processing steps. A queue-based architecture using Amazon SQS and Amazon EC2 requires self-managed compute infrastructure, unlike service-managed deployments such as AWS Batch. This architecture is best suited for loosely coupled workloads but can become complex if applied to tightly coupled workloads.

4. Hybrid Deployment
Hybrid deployments are often considered by organisations that have invested in on-premises infrastructure while also wanting to leverage AWS. This approach allows organisations to augment their on-premises resources and provides an alternative path to AWS rather than an immediate full migration. Depending on the data management strategy, AWS offers several services to support hybrid deployments. For instance, AWS Direct Connect establishes a dedicated network connection between an on-premises environment and AWS, while AWS DataSync automates data movement from on-premises storage to Amazon S3 or Amazon Elastic File System.

5. Serverless Architecture
The loosely coupled cloud journey often leads to a fully serverless environment, allowing you to focus on applications while managed services handle server provisioning. AWS Lambda can execute code without the need for server management. Additionally, other serverless architectures support HPC workflows. AWS Step Functions enable the coordination of multiple steps in a pipeline by integrating various AWS services. Serverless architectures are ideal for loosely coupled workloads or as workflow coordination when combined with other HPC architectures.

HPC Security Considerations
Security is paramount when managing HPC workloads, especially those involving sensitive data on AWS. Key considerations include data protection, access control, network security, and compliance with industry standards and regulations. AWS offers numerous security features and services, such as AWS Identity and Access Management (IAM) for access control, AWS Key Management Service (KMS) for data encryption, and AWS Security Hub for comprehensive security and compliance management.
Common Pitfalls and Challenges
Adopting AWS HPC services can present several challenges. Some common pitfalls include:
- Inadequate Planning: Without proper planning and understanding of workloads, you may select inappropriate services or resources, leading to increased costs or poor performance.
- Lack of Security Measures: Neglecting to implement robust security measures can result in data breaches or compliance issues.
- Inefficient Resource Utilisation: Not optimising for cost and performance can lead to wasted resources and increased expenses.
- Lack of Monitoring and Logging: Without proper monitoring and logging, troubleshooting issues or understanding the performance of HPC workloads can be difficult.
To overcome these challenges, it is crucial to carefully plan your HPC workloads, implement robust security measures, optimise resource utilisation, and establish comprehensive monitoring and logging in accordance with the AWS Well-Architected Framework’s HPC High Performance Computing Lens.
AWS Batch or AWS ParallelCluster?
Choosing the right AWS HPC service for your workloads depends on your specific requirements. Here’s a concise comparison to guide your decision:
- AWS ParallelCluster: This AWS-supported open-source cluster management tool simplifies the deployment and management of HPC clusters in the AWS cloud. It is designed for workloads requiring specific, complex cluster setups and supports multiple instance types, job queues, and schedulers, including AWS Batch and Slurm. With automatic resource scaling and simplified cluster management, AWS ParallelCluster is ideal for managing intricate HPC workloads.
- AWS Batch: This fully managed batch processing service is designed for workloads that require substantial batch processing and can leverage AWS’s automatic scaling capabilities. AWS Batch dynamically provisions the optimal quantity and type of compute resources based on the volume and specific resource requirements of submitted jobs.
In essence, AWS ParallelCluster offers more control and is suited for complex HPC workloads, while AWS Batch is ideal for large-scale, compute-intensive batch jobs. Your choice should align with the nature of your HPC workloads and the level of control and scalability you require.
AWS HPC Use-Cases and Success Stories
AWS HPC services have been instrumental in driving innovation for numerous enterprises, start-ups, and SMBs. For example, AstraZeneca, a multinational pharmaceutical and biopharmaceutical company, leveraged AWS Batch to optimise its genome analysis pipeline, streamlining operations and leading to significant breakthroughs in research.
Conclusion
AWS High-Performance Computing services provide a powerful suite of tools that can significantly accelerate innovation, reduce time-to-market, and address complex problems. By following best practices, leveraging the AWS Well-Architected Framework, and understanding key considerations and potential pitfalls, organisations can effectively harness the power of AWS HPC services to propel their business forward.