Covering Disruptive Technology Powering Business in The Digital Age

Written by: Nik Faiz Nik Ruzman, Journalist, AOPG.

Over the years, cloud computing has become the go-to solution for businesses seeking flexibility and scalability. It has allowed companies to achieve remarkable feats without investing in their own hardware, including adopting Artificial Intelligence (AI) capabilities. However, when it comes to truly pushing the limits of AI innovation, traditional cloud environments may not always cut it.

Companies exploring more advanced AI projects may require something more powerful. At the other end of the spectrum lie supercomputers—might they be the answer?

Joseph Yang, General Manager of HPC & AI at Hewlett Packard Enterprise

According to Joseph Yang, General Manager, HPC and AI, APAC and India at Hewlett Packard Enterprise (HPE), the difference lies in sheer computational power. “Supercomputers outperform traditional cloud architectures in handling large-scale AI models through several key advantages, like parallel processing, high-speed data access, and specialised hardware accelerators,” he explained.

These key advantages laid out by Yang imply that supercomputers if you take a moment to think about it, are actually a lot more powerful than you think. At the core of it, what makes them truly tick is their ability to harness thousands of processors working together. But it’s not just about the numbers – these systems are built for AI.

The parallel processing enables them to simulate vast amounts of complex calculations simultaneously, not to mention the support of specialised hardware accelerators like GPUs and TPUs, which handle the heavy lifting when it comes to deep learning.

Supercomputers bring the massive computational power that complex AI tasks demand. For operations working with advanced AI models, this means faster training, more accurate simulations, and the ability to tackle problems that would be nearly impossible on regular computing or cloud systems.

Engineers’ and Scientists’ Playground: When Speed and Power Matter Most

Imagine trying to sift through billions of data points or run complex AI simulations that could take months on a traditional platform. Supercomputers, however, are built to handle such massive tasks in a fraction of the time!

Yang explains, “Supercomputers can allocate all computing resources to a single workload, making it more efficient, reliable, and accurate—especially when it comes to training and tuning AI models.”

An example laid out by Yang is within the healthcare sector, aiding drug discovery, genomic analysis, and disease modelling via massive calculations and simulations at incredibly high speeds, processing vast datasets that traditional computers cannot handle.

Other use cases of supercomputers can be found across various industries as well. Scientific researchers can leverage supercomputers to achieve unique breakthroughs through complex simulations, including weather forecasting and climate modelling.

The automotive and aerospace sectors are eager to adopt this technology as well, using it for advanced simulations in vehicle design, crash testing, and aerodynamics. Even financial services utilise this technology heavily, such as in high-frequency trading, extensive risk analysis, and seamless fraud detection.

So, what can we take away from these use-case examples? One thing’s for sure; this is a life-changing mechanism that has broken the glass ceiling of enhanced data analysis and modelling capabilities.

It’s not just an improvement in terms of economic efficiency; it’s changing our lifestyle all around, giving us future possibility to discover solutions to every corner of the world’s problems that seemed hopeless to unravel for many, many years.

Power Drainers: Can They Achieve Sustainability?

Short answer – yes.

It’s easy to think of supercomputers as energy-hungry beasts, but the truth is that they’re also evolving to meet sustainability goals. Yang points out that HPE is pioneering energy-efficient supercomputers that balance performance with sustainability.

“To leverage the power of supercomputers while meeting sustainability goals requires a holistic approach to optimising energy efficiency through the design and operation process,” Yang explains.

Yang set an example of HPE’s approach which includes technologies like liquid cooling systems that reduce energy consumption by keeping supercomputers from overheating. Their QScale QO1 data centre in Quebec, for instance, runs on nearly 100% renewable energy, making it a model for future AI infrastructure.

But the real game changer? AI for data centre operations (AIOps). This approach uses AI to optimise the very systems that power AI, creating a virtuous cycle of efficiency. By streamlining resource use and identifying inefficiencies in real-time, AIOps ensures that supercomputers are not only powerful but also as green as possible. The result? An AI-driven future that doesn’t come at the planet’s expense!

Big Shoes to Fill: The Ferrari in a World Full of Fords

Not every company needs that much firepower. For smaller enterprises, supercomputers might just be overkill. It’s like buying a USD $20,000 PC to run Microsoft Word – it’ll do the job, but is it worth the spend?

So when you’re thinking about diving into the world of supercomputers, Yang breaks it down with a checklist:

1. How Big Is Your Data?
   If your business deals with massive data sets or complex simulations, supercomputers might be a game changer​.
2. Need for Speed?
   When you need fast processing times and huge simulations, supercomputers are your go-to​.
3. Training AI Models In-House?
   If you’re building advanced AI models from scratch, supercomputers offer the power you need to get it done​.
4. Do You Have the Right People?
   Running a supercomputer isn’t easy. “Only 10% of enterprises have the expertise to handle supercomputing resources effectively,” Yang points out​. For the rest, partnering with a company like HPE is a smart move.

The Bigger Picture: Extremely Powerful, But Not for Everyone

Supercomputers are undeniably powerful, but they’re not for everyone. If you’re running smaller AI projects and lack the infrastructure to support such a system, a combination of on-premises solutions or standard cloud services might be all you need. With Yang’s insights, one thing we can summarise is – supercomputers are for enterprises with large-scale AI ambitions. If your AI workload isn’t pushing the envelope, you might be better off sticking with cloud solutions or hybrid infrastructures.

In the coming years, supercomputers will undoubtedly play an important role in solving some of the world’s most complex problems. While not every business may require this level of computing today, staying aware of advancements in this field will ensure you’re prepared for the future. As the demands for greater processing power grow, so too will the potential for supercomputers to shape industries and drive breakthroughs.

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