Find out how GPU-accelerated compute is helping organizations run more HPC workloads faster, in a more energy-efficient way.

February 26, 2024

3 Min Read
iStock via AWS

By AWS and NVIDIA

Across industries cloud-based high-performance computing (HPC) is on the rise. According to a recent study by Hyperion Research, organizations are increasingly choosing the cloud to run their HPC workloads. In fact, nearly every organization adopting HPC resources is either already using or is investigating the cloud to accelerate HPC workloads. The cloud market for HPC resources is expected to grow at more than twice the pace of the on-premises server market, topping $11 billion by 2026.

Some of the main reasons a cloud approach is growing in popularity is that it enables organizations to dynamically scale and optimize compute infrastructure to match fluctuating HPC workload requirements. Scalable HPC capacity allows users to run business-critical workloads without waiting in queues, and pause the compute once the job runs complete, helping optimize compute infrastructure consumption to remain agile and energy-efficient.

Convergence of Cloud, HPC, and AI/ML

A new category of HPC workloads is emerging. HPC users are adopting and integrating artificial intelligence (AI) and machine learning (ML) at increasingly higher rates. Multiple methods and models exist with large language models (LLMs) and a number of foundation models (FMs), drawing broad global interest from organizations.

Hyperion Research found that nearly 90% of HPC users surveyed are currently using or plan to use AI to enhance their HPC workloads. This includes hardware (processors, networking, data access), software (data management, queueing, developer tools), AI expertise (procurement strategy, maintenance, troubleshooting), and regulations (data provenance, data privacy, legal concerns).

As a result, organizations are experiencing a convergence of cloud, HPC, and AI/ML. Two simultaneous shifts are occurring: one toward workflows, ensembles, and broader integration; and another toward tightly coupled, high-performance capabilities. The outcome is closely integrated, massive-scale computing accelerating innovation across industries from automotive and financial services to healthcare, manufacturing, and beyond.

The Benefits of Running HPC Workloads in the Cloud

Cloud-based accelerated computing provides organizations with faster time to results, helping increase energy efficiency as the resources that consume energy can perform more jobs, faster.

HPC users can access the latest technological advances such as HPC SDKs and scale them to a larger number of parallel jobs. For example, in the automotive industry, computational fluid dynamics (CFD) simulations are needed to reproduce the behavior of autonomous vehicles in various conditions. By utilizing accelerated computing in the cloud, manufacturers can run these simulations in parallel without disruptions, compared to performing expensive and risky real-life tests. HPC clusters can be paused in minutes, ensuring no resources are left unused after simulation runs complete.

Moving HPC workloads to the cloud, HPC users can adopt new AI applications in their workflows to gain insights faster. Accelerated computing libraries, frameworks, and SDKs improve the technical capabilities of HPC users across their end-to-end workflow empowering scientists and engineers to overcome the challenges of deploying HPC workloads efficiently at scale.

HPC infrastructure, tools, and services in the cloud provide a full-stack solution for HPC users that empower HPC workloads and allow organizations to scale clusters. HPC teams can efficiently run hundreds of thousands of batch and ML computing jobs while optimizing compute resources with faster networking, lower latency, and lower jitter connections when running tightly coupled applications.

Move Forward With Cloud-Based HPC Workloads

As HPC workloads increase in complexity, accelerated computing in the cloud is needed to ensure scalability and performance. Choosing the right compute, memory, and network performance to help reduce the time to complete HPC jobs, while maximizing energy-efficiency, is not an easy task. By moving to the cloud, HPC teams can use scalable infrastructure, acceleration tools, and services to meet HPC workload requirements, secure infrastructure, and remain energy efficient.

Learn more about how AWS and NVIDIA can help accelerate HPC workloads.

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