Why HPC in the Cloud?

June 10, 2024

Running demanding workloads in the cloud empowers in-house HPC teams and end users alike.

Innovation across many industries is driven by deep insights which in turn depend on increasing amounts of compute power.

High performance computing (HPC) enables the world’s sharpest minds to design, test, and optimize new products and services while making existing processes better, faster, and more energy efficient. From automotive ingenuity to life-saving drugs, HPC-powered innovations have a huge impact on individuals, society, and the environment at a global scale.

However, traditional on-premises HPC can often come with high upfront costs, limited ability to scale, and burdensome management of HPC physical hardware.

Add the cloud, though, and HPC suddenly becomes a lot more accessible, and powerful.

Most notably, cloud HPC is accelerating the AI revolution by providing access to the most sought-after infrastructure and services for model training and inference, something almost every industry need. Cloud HPC is propelling high-value, highly demanding workloads across manufacturing, healthcare and life sciences, weather and climate science, and beyond. Crucially, cloud HPC is helping organizations harness the full potential of their most expensive, most valuable assets: people.

That doesn’t just mean researchers, engineers, scientists, and other end-users. In-house DevOps teams, systems administrators, and HPC experts can also gain from a cloud-based approach.

Here’s why.

High performance, high demands

Domain specialists use HPC for distributed deep learning, engineering analysis, and complex simulations. Behind them, making it all possible, are trusted HPC support teams. These specialists are geared to managing niche applications in often bespoke environments.

As they know all too well, on-premises HPC infrastructure can be resource-intensive, both in terms of people and capital. Provisioning and coordinating the necessary hardware, software, and networking capabilities is daunting for the experts, let alone the uninitiated. With the rapid pace of innovation and increasingly demanding workloads, it can be challenging to keep infrastructure up to date, let alone afford it.

HPC for anyone, with any workload, at any time

Ideally, HPC administrators want to ensure anyone can access compute whenever and wherever they need to. At the same time, they must also balance unpredictable usage demands against the fixed costs of acquiring, managing, maintaining, and upgrading on-premises infrastructure.

HPC teams are incentivized to keep on-premises infrastructure running 24/7 to recoup their investment. To avoid idle time and ensure fair access to all users, it makes sense to book resources well in advance. Unfortunately, many workloads don’t stick to neat timetables. When a big new job is required quickly, as so many are in today’s rapidly moving research sectors, users typically have to wait in a queue for days, weeks, or even months. Either that or patient users waiting for pre-booked slots get bumped to the back of the queue, and that hurts an organization’s ability to stay ahead of the competition.

Overprovisioning negatively impacts ROI. But underprovisioning conceals a heavy, long-term opportunity cost.

Research and development is a naturally elastic environment. The next breakthrough design or life-saving drug can’t wait in a queue or for the next procurement cycle. It shouldn’t be limited by a failure to accurately predict technology trends five years in advance. Of course, five years is nothing compared to how long true expertise takes to develop. It can take decades to train a scientist or an engineer—not to mention finding and hiring one. This means the most expensive part of any R&D workflow is never the infrastructure: it’s the person waiting for the result.

So, in-house HPC teams are seemingly left with two choices, each with unacceptable tradeoffs:

Do we invest in spare capacity that may go unused for long periods? Or do we maximize usage of minimal resources and accept long queues, disruption, disgruntled users, and delayed innovation?

Fortunately, there is another option.

Cloud HPC empowers everyone

The cloud lowers the barrier to entry for HPC workloads while removing the limits to innovation.

All the core benefits of the cloud apply: flexibility, scalability, near limitless capacity, world-class security, efficiency, and pay-as-you-go pricing.

Organizations get the resources they need, when they need them, with the ability to scale up and down seamlessly and ensure on-demand capacity for urgent and peak workloads. Users enjoy anytime, anywhere access to cutting-edge infrastructure now—including the latest GPUs, CPUs, and hardware accelerators as they become available—instead of waiting for the next refresh cycle (or a bigger budget).

Experimentation becomes easy and affordable. Deadlines are kept. Budgets are simpler to manage, and organizations can run ever larger, more complex simulations for deeper insights and better results.

Meanwhile, IT teams spend less time buying, building, and owning systems—and more time helping end-users solve ever-harder problems. Because end users don’t have the time or expertise to spin up and allocate HPC resources themselves—they just need reliable, easy access. With cloud HPC, in-house HPC teams can better serve their colleagues with the latest hardware, software, network capabilities, and accelerated instances for today’s demanding workloads.

Cloud HPC also enhances collaboration and accessibility for organizations. With remote access to HPC resources, distributed teams can work together seamlessly, facilitating collaboration across geographies.

What could be more valuable in a time of scarce talent and intense demand?

What will organizations achieve?

Cloud HPC supports diverse use cases across multiple sectors, from designing electronic vehicles to discovering new molecules for dug development, running complex financial simulations, and modeling cellular processes.

Key workloads include:

  • Computer-aided engineering (including fluid dynamics, combustion, crash safety, structural mechanics)
  • Computational physics and chemistry
  • Electronic design automation (EDA)
  • Quantitative analysis and risk analytics
  • Special effects rendering
  • Drug discovery

In addition to top-tier infrastructure, organizations can access best-in-class partners for specialist tasks. Notably, HPC is essential for the latest artificial intelligence (AI) and machine learning (ML) workloads. HPC and AI/ML are converging to focus on two key areas: workflows, ensembles, and broader integration, and toward tightly coupled, high-performance capabilities. In particular, this will prove vital in training and deploying the next generation of large language models (LLMs) and foundation models (FMs) at the forefront of so much innovation.

Nearly 90% of HPC users are currently using or plan to use AI to enhance their HPC workloads[1]

New use cases are being discovered all the time. HPC has already expanded beyond the niches that created it. Cloud infrastructure, built as code, makes it possible—in fact, normal—to deploy insights and analytics powered by HPC to clinicians in hospitals or first responders in disaster zones.

In just a few years, HPC in the cloud has grown to be the innovation driver behind tackling some of the world’s biggest problems and helping organizations decrease time-to-market for new products and solutions.

Find out more at our HPC event.


[1] Hyperion-Research-SC23-Briefing-Novermber-2023_Combined.pdf (hyperionresearch.com)

 

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