How HPC and AI Convergence Enables Business Benefits

High-Performance Computing (HPC) and Artificial Intelligence (AI) Defined

High-performance computing (HPC) refers to computing operations that run at extremely high speeds and typically with parallel processing made possible by a supercomputer or data center. It’s the technology that underlies much of big data and analytics, including machine learning (ML), wherein systems “learn” by trial and error at scale, finding optimal solutions by brute force.

Artificial intelligence (AI) is a different sort of technology. It enables machines and computers to emulate human capabilities in problem-solving, decision-making, and intelligence more broadly. AI encompasses numerous technologies, including computer vision, natural language processing (NLP), and, more recently, generative capability.

Why HPC and AI Need to Converge

HPC (High Performance Compute) and AI have major overlap in terms of use cases and value they can potentially deliver to a business. They also rely in general terms on similar hardware. Yet these two disciplines are typically siloed within organizations that have both.

Convergence here can lead to significant advantages for firms that do it properly. In a recent report, Forrester explained in a recent report entitled AI Plus HPC, how these advantages relate synergistically:

• Big data can bring new life to neural networks, enabling better deep learning (AI) based on better data.
• Deep learning (AI) can improve the results that organizations get from HPC by reducing the load of processing certain data types.
• HPC provides the computing power necessary to make it all happen.

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Benefits of HPC and AI Convergence

HPC and AI convergence promises to deliver numerous benefits to organizations, including these four.

• Cost reduction – By bringing HPC and AI into the same shared infrastructure, organizations can reduce costs. Currently, these disciplines are typically siloed, requiring independent infrastructure, which must be separately set up, maintained, and paid for. Bringing HPC and AI together reduces the need for maintaining separate infrastructure. Another angle to the cost reduction benefit is reduced compute needs. For example, AI can help to increase model targeting accuracy and identify ways to improve HPC workflows, reducing the compute need of those workflows.
• Competitive advantages – Combining HPC and AI also reduces the time it takes to develop insights, which delivers a competitive advantage. HPC compute power can speed up the process of training ML models and, as noted above, AI can optimize HPC workflows. The end result includes identifying insights faster, supporting quicker and more accurate decision-making.
• New insights – By un-siloing these two disciplines, organizations can find new ways to innovate by reaching insights that would otherwise have gone unnoticed. HPC is well adapted to deal with problems requiring complex calculations, while AI is well suited for dealing with uncertainty, missing data, and unstructured data (such as videos, photos, natural language documents, and sensor data). Combining the two can deliver new insights that can propel an organization forward.
• More agility – Last, combining HPC and AI into a centralized unit delivers greater agility. As organizations further optimize workflows and better balance resource loads, they gain the ability to move and even change direction more quickly. With numerous disciplines operating within a single environment, organizations can reshape that environment without the red tape of having to work through the effects with every other siloed department or work group.

Challenges to HPC and AI Convergence HPC and AI Convergence

Though we see great upside to the combination of HPC and AI, it won’t happen without overcoming certain challenges.

• Infrastructure is lacking – First, the infrastructure required for next-gen AI is significant, and most companies aren’t prepared. Most IT experts interviewed state they don’t even have enough compute power now, let alone the question of what happens when AI gets added to the same infrastructure. Of course, infrastructure is solvable. But making the necessary investments is a must for businesses seeking the strategic advantages combing from AI.
• AI still early in its development – AI has been surrounded by a stunning amount of hype. And while it’s unwise to ignore the potential, it is prudent to admit that this technology is still in its early stages in terms of enterprise usefulness. Developing a plan to implement and ensuring you have the right goals and steps towards an AI Project can help with positive results from an AI project.
• Data silos harm effectiveness – In most organizations, disciplines and departments aren’t the only thing that gets siloed. So does data. For AI and HPC convergence to produce the most effective analytics results, these converged capabilities will need access to the right data in the right forms. Un-siloing that data (while still managing to keep it organized and optimized) is an ongoing challenge.Organizations like HPE that provide best-in-class HPC technology and trusted infrastructure platforms like their HPE Greenlake can help make AI projects more successful because they understand how to effectively combine AI and HPC.

Start Your HPC and Convergence Journey With Comport

As we look toward the future of advanced analytics, the path is clear: HPC and AI will converge for most businesses, forming a combined discipline that produces next-generation results across numerous facets of the company.

Now is the time to start your HPC and AI convergence journey. Organizations are starting to see the advanced benefits of AI but choosing the right partner for this journey is key. Comport has access to AI, data and infrastructure experts to ensure you reach your goals and your destination.