High-Performance Computing (HPC) on AWS

A Sectoral Perspective

Energy Sector + HPC

The energy sector has long utilised the most powerful computers for systemic analysis in their quest for accessible oil and gas deposits. The enormous amount of data required to comprehend geological formations beneath the Earth’s surface has led to the energy sector adopting High-Performance Computing (HPC) applications in the cloud. This not only enables faster results but also presents a cost-effective solution. For instance, a seismic data file could be between 10 to 15 terabytes, making it challenging, if possible, to run applications on a single machine.

Applications of HPC in Various Fields

Scientific Research

In scientific research, there is a need to generate detailed images of individual biomolecules to understand the functioning of the human body. Researchers must comprehend how proteins move throughout the body and observe molecules in their native state. This necessitates the integration of intelligence, modelling, and genomics, requiring supercomputers to analyse approximately one terabyte of data daily.

Financial Services

In financial services, adherence to regulations makes cloud migration more complex. Yet, the sector demands substantial computing power to increase the accuracy of risk analysis and replace manual tasks with artificial intelligence capabilities. Other sectors, such as academic institutions, life sciences, defence, and manufacturing firms, also require immense power to run their operations reliably and generate precise results.

Amazon Web Services HPC provides high-performance computing for such diverse business use cases. Despite relying on the foundational building blocks of AWS, such as S3, EC2, security groups, and VPC, certain design principles make HPC a powerful computing platform.

Design Principles of AWS HPC

Dynamic Architectures

As data grows and evolves, dynamic architectures become vital. AWS HPC aids in avoiding long queues, allowing for efficient code applications and smoother installations. Before designing your architecture, it is crucial to have a clear picture of the data’s origin, its size, velocity, and how frequently it is updated.

Holistic Optimisation

Holistic optimisation of performance and cost is a primary focus. It includes computation and, increasingly, automation. Modern HPC incorporates a large amount of meta-code, allowing for the creation and replication of systems at a lower cost, bypassing manual overheads. You can track changes to your code, understand the impact, and revert to previous versions when necessary.

Collaboration

HPC work often occurs in a collaborative context, sometimes spanning many countries. This is evident in scenarios such as weather reports or financial services, which require global collaboration. Besides close cooperation, tools, code, and data are often shared with the wider HPC and scientific community.

Testing

Finally, always test real-world workloads. The only way to understand how your production workload will perform in the cloud is to test it on the cloud. Most HPC applications are complex and have specific memory, CPU, and network requirements, so they cannot be deduced from a simple test. You need to utilise real-time workloads and data.

Conclusion

High-Performance Computing runs exceptionally well on well-defined architectures. These architectures have specific characteristics that make them ideally suited for deploying applications in the cloud. They can fine-tune cloud resources and create cloud-native architectures that naturally accelerate the turnaround and the return on investment of HPC workloads. AWS HPC presents a promising avenue for diverse sectors seeking to leverage the power of high-performance computing in their operations.