Many industries are starting to run HPC in the cloud. Find out how GPU-accelerated compute is helping organizations run more workloads faster, energy efficiently.

March 4, 2024

3 Min Read
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Hyperion Research projects that the HPC market will reach $33 billion in 2023 and pass $50 billion by 2026, as the demand for high-performance computing (HPC) capacity increases.

The adoption of more powerful GPUs helps accelerate artificial intelligence (AI) and machine learning (ML) workloads but running accelerated computing increases energy demand. Last year, global electricity markets experienced a rise in prices, causing heightened focus on energy-efficient solutions. Increases in energy prices raises concerns about limited power capacity in data centers and the ability to run complex HPC simulations. As a result, organizations are looking for new ways to run their HPC workloads securely, at scale, while reducing energy consumption.

Convergence of Cloud, HPC, and AI/ML

HPC workloads have been experiencing a shift, with a new category emerging. As HPC users are increasingly integrating AI/ML technologies into their workloads, the interest in methods and models existing with large language models (LLMs) and foundation models (FMs) is growing.

Hyperion Research found that nearly 90% of HPC users surveyed are currently using or plan to use AI to enhance their HPC workloads. These enhancements can be implemented on multiple levels including 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, the cloud, HPC, and AI/ML are converging with two simultaneous shifts. The first one is toward workflows, ensembles, and broader integration; and the second shift is toward tightly coupled, high-performance capabilities. The outcome is tightly integrated massive-scale computing accelerating innovation across industries from automotive and financial services to healthcare, manufacturing, and beyond.

Achieve HPC Workload Energy Efficiency in the Cloud

Running HPC in the cloud enables organizations with accelerated computing technology, tools, and services on-demand. Scientists and engineers can run their HPC workloads without waiting in a queue. Cloud-based HPC brings flexibility to run workloads at scale on the latest technology, helping complete workloads faster for typically the same amount of energy consumed.

To overcome limited power capacity, organizations can scale out HPC and AI/ML workloads to run on thousands of GPUs. GPU-accelerated computing with low-latency and high-bandwidth networking helps HPC users manage power allocation by running jobs faster leading to faster time to results and freeing up resources for other compute needs.

Cloud-based HPC with access to AI/ML tools and services improves performance for HPC applications and can reduce overall energy consumption by running HPC simulations faster.

Driving Energy-Efficiency with HPC in the Cloud

Accelerated computing in the cloud improves the performance of HPC workloads. By shortening the processing time of HPC applications and increasing performance, organizations can maximize the amount of computational work completed for typically the same amount of energy consumed.

HPC-optimized instances running on the latest GPUs can speed up solution development while services and tools along with accelerated computing libraries and frameworks enable organizations to innovate quickly at scale.

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

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