Modern high-performance computing (HPC) refers to the ongoing transformation of traditional HPC through its convergence with artificial intelligence, machine learning (AI/ML), high-performance data analytics (HPDA) and other emerging technologies, as well as the expansion beyond the primary data center.

Yeah, we know – that was a lot. But it’s really more straightforward than it sounds.

Modern HPC is a shift in the way that we conceptualize and do HPC. Think of it like how we went from the kitchen landline to today’s smartphone – it still performs the same primary function, of course, but now it’s physically untethered and digitally integrated with various applications.

How did we get here?

Once upon a time (and by that, we mean before the ‘90s), HPC was confined to the realm of academia and research. Since then, though, we’ve watched as it has ventured into countless sectors and left a trail of innovation and transformation in its wake.

Take manufacturing, for example. In the past, creating a new product involved countless prototypes, extensive trial and error, and a significant investment of time and resources. But with the growing adoption of HPC, virtual prototyping and simulation have become the norm. Complex designs can be tested and optimized virtually, enabling engineers to fine-tune their creations before a single physical prototype is built. This not only slashes costs but also greatly accelerates time-to-market, giving manufacturers a competitive edge like never before.

But as HPC became more prevalent and more critical to business operations, the time came to rethink our approach toward it. It could no longer be a science project or a back-end processing activity. It had to become something different – more reliable, more flexible, and much less complicated. And that’s exactly what we mean when we talk about modern HPC.

To sum it up, modern HPC is the result of three major developments that have come out of today’s data- and compute-intensive workflows:

• Goodbye, HPC complexity. In today’s market, HPC has become essential to organizations seeking to gain a competitive edge (or even to just stay afloat), which means that reliable and simple HPC solutions are more important now than ever. Gone are the days when high-performance data storage and management were reserved for academic researchers or specialized experts, and having a dedicated HPC storage specialist stationed at every data location just isn’t feasible.
• Data on the move. The data for high-performance applications is being stored and processed in different locations today. Organizations are utilizing cloud resources and pushing computational capabilities to the edge where their data is generated, allowing for real-time decision-making and greater agility. As data locations continue to expand, data visibility and mobility have become crucial components in optimizing HPC environments.
• AI has entered the chat. The introduction of AI/ML techniques to HPC workloads has brought new demands and challenges. Organizations are increasingly running multiple HPC and AI/ML applications with diverse IO patterns and file sizes all at once, also known as “mixed workloads.” Without a storage solution that optimizes data placement and delivers high performance across a range of application types, achieving the data access those mixed workloads demand can quickly become an issue.

The evolution of HPC has enabled analysts, developers, and researchers to access and analyze massive volumes of data from a variety of sources using advanced algorithms and techniques that were previously out of reach. Modern HPC is revolutionizing how we generate renewable energy, tackle climate change threats, and design autonomous vehicles.

Let’s take a step back and break down exactly how this version of HPC differs from the HPC of yesterday.

How does modern HPC differ from traditional HPC?

Up until about ten years ago, when someone said “HPC,” you knew they were talking about complex modeling and simulation applications running on large supercomputers or clustered HPC systems powered by central data centers. While this is still a vital aspect of HPC today, modern HPC has been rapidly evolving to cover much more ground.

• Expanding data locations

In traditional HPC, data was typically stored in a single location on a set of storage servers. Today, we have the expansion of data processing and storage beyond core data centers to include the cloud and the edge. This expansion allows organizations to execute HPC workloads closer to data sources, enabling faster processing and reducing data transfer latency, and offers them better resource utilization and cost efficiency.

• Data visibility and mobility

As HPC has evolved, there has been a greater emphasis on data visibility and mobility. Modern HPC systems now provide seamless access to data across different locations and platforms. Data visibility gives you a holistic view of your data across all platforms, so you always know what you have and where you have it, while data mobility facilitates the easy movement of data between your core data centers, cloud environments, and edge devices.

• Management simplicity

In the past, HPC systems required specialized experts to manage multiple complex storage systems and optimize application performance, which involved time-consuming tuning and retuning as well as long (and costly) periods of system downtime. Even today, complexity and lack of reliability remain pain points for many HPC systems, particularly open-source parallel file systems such as Lustre. Modern HPC solutions, in contrast, prioritize user-friendly interfaces, automation capabilities, and streamlined workflows that enable non-experts to manage and operate HPC infrastructure easily.

• Breaking down data silos

Data silos – where information and datasets are isolated within specific departments or research groups – were the norm in traditional HPC. Modern HPC recognizes the value of collaboration and aims to break these down. The goal is to promote secure data sharing and integration across teams, fostering cross-disciplinary research, uncovering hidden insights, and driving innovation.

• Mixed workloads

In addition to traditional modeling and simulation applications, modern HPC supports a broader range of workloads. This includes HPDA, AI/ML, and other data-intensive tasks. Modern HPC solutions can efficiently handle diverse workloads, enabling users to leverage multiple tools and techniques within a unified environment.

Modern HPC in action

• Healthcare and Life Sciences. Modern HPC powers advanced medical image analyses, genomics research, and drug discovery. HPC systems can use AI to quickly analyze medical images, assisting doctors in diagnosing diseases with higher accuracy and speed. In genomics research, HPC facilitates the processing and analysis of large genomic datasets, helping identify genetic patterns associated with diseases and potential treatment options. HPC-powered simulations accelerate drug discovery by simulating the interactions between different compounds and targets.
• Manufacturing. Modern HPC plays a vital role in optimizing designs, simulations, and modeling in manufacturing. HPC systems can leverage AI to analyze sensor data and detect anomalies in manufacturing processes, enabling predictive maintenance to prevent equipment failures. ML models can be employed to optimize designs, material usage, and production processes, leading to improved efficiency and reduced costs.
• Financial Services. Modern HPC is used in the financial sector for risk analysis, algorithmic trading, and fraud detection. HPC systems utilize AI/ML to process large financial datasets, identifying complex patterns and delivering accurate risk assessments. ML models can detect fraudulent activities by analyzing massive volumes of transaction data, and HPC enables algorithmic trading platforms to process real-time market data and execute high-frequency trades based on sophisticated ML-driven strategies.
• Energy. Modern HPC is used in the energy sector for oil and gas exploration, renewable energy optimization, and grid management. Organizations use AI to process massive amounts of seismic data and identify geological anomalies accurately and efficiently, which streamlines the exploration process, reduces costs, and improves the success rate of finding valuable reserves. Additionally, HPC aids in optimizing renewable energy resources by analyzing weather patterns and improving grid management for more efficient renewable energy distribution.
• Academic Research: Modern HPC plays a critical role in research across disciplines such as physics, astronomy, agriculture, materials science, and beyond. Researchers leverage HPC systems to simulate complex phenomena and analyze massive datasets.

Modern HPC solutions by VDURA

The VDURA name is new, but we’ve actually been in the HPC game for a long time – over 25 years now – so we understand the intricacies of HPC data storage and management like no other.

Our extensive knowledge of this domain combined with our drive to stay ahead of the curve led us to develop solutions that truly address the HPC and AI/ML challenges that our customers are facing today. That’s why we’ve designed an HPC and AI data platform that is unrivaled in its comprehensiveness, simplicity, and reliability.

To learn more about the VDURA Data Platform, click [here].