What Is High Performance Computing (HPC)?

When scientists need to model climate change over decades, when engineers test vehicle designs through millions of simulated crashes, when enterprises train Generative AI models reshaping industries, or when researchers sequence entire human genomes in hours rather than years, they turn to high performance computing. 

These aren’t just faster computers running the same old programs. HPC represents a fundamental shift in how we approach computational problems that would take traditional systems months or even years to solve.

High performance computing (HPC): key takeaways

• HPC uses clusters of interconnected computers working in parallel to solve complex computational problems much faster than traditional systems
• Modern HPC systems can perform quadrillions of calculations per second through massively parallel processing across thousands of nodes
• Modern AI is now virtually synonymous with HPC; high performance clusters provide the essential foundation required to train and run Generative AI and Large Language Models (LLMs)
• HPC powers critical applications across industries, from drug discovery and climate modeling to fraud detection and autonomous vehicle development
• Cloud-based HPC solutions have made this technology more accessible and affordable for organizations of all sizes
• SUSE Linux Enterprise Server provides the stable, scalable HPC foundation needed to run AI/ML and demanding workloads across diverse hardware architectures

What is high performance computing (HPC)?

High performance computing is a class of workloads that aggregates computing power from multiple interconnected systems to process massive datasets and solve complex problems at speeds far beyond what standard computers can achieve. Today, HPC and AI have become virtually synonymous; there is no modern AI strategy without the high-performance infrastructure required to train and run it. 

Unlike your laptop that processes tasks sequentially, HPC systems divide computational work into smaller tasks and execute them simultaneously across hundreds or thousands of processors.

Think of it this way: if one person took a week to count all the grains of sand on a beach, a thousand people working together could finish in minutes. HPC applies this same principle to computational work. A typical desktop processor might perform around 3 billion calculations per second, which sounds impressive until you need to simulate molecular interactions, predict weather patterns, train Large Language Models (LLMs) or power Generative AI. HPC systems routinely perform quadrillions of calculations per second, making previously impossible analyses suddenly practical.

The technology relies on three core components working together seamlessly. Compute resources provide the processing power through specialized CPUs and GPUs. High-speed networking connects these resources with minimal latency, allowing quick data transfer between nodes. Storage systems keep up with the entire infrastructure, feeding data to processors and capturing results without creating bottlenecks.

How does HPC work?

At its heart, HPC operates through massively parallel computing. Traditional computers work serially, completing one task before moving to the next. HPC systems take a fundamentally different approach by breaking large problems into thousands of smaller pieces that can be solved simultaneously.

HPC clusters

An HPC cluster brings together individual servers (called nodes) into a unified system that functions as a single powerful computer. Each node contains its own processors, memory and storage, but they’re all networked together through high-performance connections. When you submit a job to an HPC cluster, specialized software distributes the work across available nodes, coordinates their activities and assembles the final results.

The number of nodes in a cluster can vary dramatically based on the workload requirements. Some clusters contain just dozens of nodes for departmental use, while the world’s largest supercomputers link together hundreds of thousands of nodes. This modular architecture gives HPC systems their characteristic scalability.

Key components of HPC: compute, network & storage

HPC systems need three components working in harmony to deliver peak performance. The compute layer typically relies on high-core-count CPUs optimized for parallel processing, often supplemented with GPUs or other accelerators for specific workload types. Modern HPC nodes might contain dozens of processor cores, multiple GPUs and hundreds of gigabytes of memory.

The network component connects all these resources through specialized fabrics designed for minimal latency and maximum throughput. Technologies like InfiniBand or high-speed Ethernet create the communication pathways that let nodes share data and coordinate their work. Without fast networking, even the most powerful processors would spend most of their time waiting for information rather than computing results.

SUSE Linux Enterprise Server provides the operating system foundation for HPC that ties these hardware components together, supporting everything from x86-64 to Arm processors and specialized GPU platforms optimized for classical HPC and modern AI/ML workloads.

HPC and the cloud

Cloud computing has transformed HPC accessibility. Organizations no longer need to invest millions in on-premises infrastructure and maintain specialized facilities with extensive cooling and power systems. Instead, they can access HPC resources on demand through major cloud providers like Microsoft Azure and AWS.

This shift to cloud-based high performance computing means researchers and businesses can scale resources up or down based on project needs. You might run intensive simulations using thousands of cloud-based processors for a few days, then scale back to minimal resources when analysis work doesn’t require such power. However, most modern organizations adopt a hybrid approach, keeping steady-state workloads on-premises for cost control while bursting to the cloud for peak demands.

AI needs HPC: The Foundation of Modern Intelligence

While often discussed separately, HPC and AI are inextricably linked. Today, there is no Artificial Intelligence strategy without the High Performance Computing infrastructure to support it.

This is because AI is essentially a massive math problem. Training a Large Language Model (LLM) or a Generative AI application involves feeding it petabytes of data and requiring quadrillions of matrix calculations to establish patterns. A standard enterprise server farm would take decades to complete a single training run.

HPC clusters are the only systems capable of handling this load. By utilizing massive parallel processing and high-speed interconnects (like InfiniBand), HPC condenses this training time from years into weeks or days. Whether it’s a chatbot answering customer queries, a coding assistant for developers or a computer vision system in a factory, the “intelligence” is ultimately powered by an HPC foundation.

Why is HPC important?

The explosion of data in our world has created challenges that traditional computing simply cannot handle efficiently. Every day, organizations generate and collect massive datasets from sensors, instruments, transactions and user interactions. Making sense of this data and turning it into insights that drive decisions requires computational power that only HPC can provide.

Speed matters tremendously in many applications. Weather forecasting relies on HPC to process atmospheric data and run complex models quickly enough to provide useful predictions. Financial institutions use HPC to analyze market trends and execute trades in microseconds. Medical researchers need HPC to process genomic data and identify disease markers before patients’ conditions worsen. In each case, getting answers faster doesn’t just save time but can also save lives or provide crucial competitive advantages.

HPC also makes previously impossible research feasible. Scientists can now simulate protein folding to understand diseases and design new drugs, model entire galaxies to understand cosmic evolution or test thousands of vehicle designs virtually before building a single prototype. These applications would take traditional computers months or years to complete, making them impractical for real-world use.

HPC use cases: what does high performance computing do in the real world?

HPC powers innovation across virtually every scientific and industrial field. The technology has become so integral to modern research and business operations that many breakthroughs simply wouldn’t happen without it.

Product design and simulation in manufacturing

Manufacturers use HPC to dramatically reduce product development time and costs. Instead of building dozens of physical prototypes and putting them through expensive real-world tests, engineers create detailed digital models and run thousands of simulations. These virtual tests can evaluate everything from structural strength under stress to aerodynamic performance to thermal characteristics.

MTU Aero Engines uses HPC underpinned by SUSE Enterprise Linux Server to improve the predictive accuracy of their simulations, allowing them to optimize jet engine designs faster and more cost-effectively than traditional testing methods. The computational fluid dynamics simulations they run would be impractical without HPC’s parallel processing capabilities.

Automotive companies similarly rely on HPC for crash testing simulations, aerodynamic optimization and increasingly for ingesting petabytes of sensor data to train the AI systems that power autonomous vehicles. Each virtual crash test that replaces a physical one saves both time and money while providing more detailed data about vehicle performance.

Climate modelling and weather prediction

Climate scientists process huge amounts of historical meteorological data and run complex simulations that model atmospheric behavior, ocean currents and countless other variables. These models divide the Earth’s atmosphere into millions of three-dimensional grid cells, calculating how conditions in each cell interact with neighboring cells over time.

The computational demands are staggering. A single climate model might need to perform trillions of calculations to simulate even a few years of weather patterns. Weather forecasting services run these models multiple times daily, ingesting fresh data from satellites, ground stations and ocean buoys to continually refine their predictions. Without HPC’s parallel processing power, forecasts would take so long to calculate that they’d be obsolete before completion.

Protein folding, human genomics and other medical applications

The first human genome sequencing took over a decade and cost hundreds of millions of dollars. Today, HPC-powered systems can sequence a genome in hours for less than a thousand dollars. This dramatic improvement has opened new frontiers in personalized medicine, where treatments can be tailored to patients’ specific genetic profiles.

Protein folding research represents another area where HPC has driven major breakthroughs. Understanding how proteins fold into their three-dimensional shapes is crucial for drug development, but the number of possible configurations grows exponentially with protein size. HPC systems can simulate these folding processes and predict protein structures, accelerating the development of new treatments for diseases ranging from Alzheimer’s to cancer.

Medical imaging also benefits from HPC, with advanced systems processing CT scans, MRIs and other diagnostic data to detect diseases earlier and more accurately than human analysis alone.

Fraud detection in financial services

Financial institutions process billions of transactions daily, and identifying fraudulent activity within this massive data stream requires real-time analysis that only HPC can provide. Modern fraud detection systems use machine learning models trained on historical data to identify suspicious patterns. These models must evaluate multiple factors for each transaction—location, amount, timing, merchant type, user behavior patterns—and make accept/reject decisions in microseconds.

Beyond fraud detection, financial services firms use HPC for risk analysis, portfolio optimization, options pricing and algorithmic trading. The ability to analyze market conditions and execute trades faster than competitors provides measurable financial advantages.

HPC needs a stable foundation

All this computational power needs a rock-solid operating system foundation to deliver reliable results. High Performance Computing (HPC) systems can’t afford downtime or instability when running calculations that might take days or weeks to complete.

SUSE Linux Enterprise Server provides that foundation with several key advantages. The platform supports ultimate hardware flexibility and prevents vendor lock-in, running efficiently on x86-64, Arm and essential GPU and accelerator platforms. This versatility allows you to choose the most suitable and cost-effective hardware for your specific workloads rather than being locked into a single architecture.

The system comes optimized for hybrid cloud deployments, making it easy to burst from on-premises infrastructure to major public clouds when you need additional capacity. SUSE Linux Enterprise Server is the perfect OS to run complex AI/ML workloads, data analytics and simulation applications. Combined with support from a broad partner ecosystem, the platform delivers a certified and future-proof stack optimized for the largest supercomputers and cutting-edge hardware.

Learn more about SLES today to see how a stable, scalable foundation can accelerate your most demanding computational workloads.

High performance computing FAQs

Why should businesses invest in HPC?

Businesses invest in HPC because it solves problems that traditional computing cannot handle efficiently, if at all. The technology reduces product development cycles, enables data-driven decision making at scale, powers AI and machine learning applications and provides competitive advantages through faster time to insight. For many organizations, HPC has shifted from a nice-to-have research tool to a business-critical capability.

How is HPC related to Artificial Intelligence (AI) and Generative AI?

HPC is the engine that powers modern AI. Training Large Language Models (LLMs) and Generative AI applications requires processing petabytes of data and performing quadrillions of calculations—a task that would take standard servers years to complete. HPC clusters provide the massive parallel processing power and high-speed GPU interconnects needed to train these models in days or weeks, making them the essential infrastructure for any scalable AI strategy.

Can SUSE solutions support my HPC needs?

Yes. SUSE Linux Enterprise Server provides comprehensive support for high performance computing workloads across diverse hardware platforms. The system integrates with leading HPC file systems, supports both on-premises and cloud deployments and includes optimizations specifically designed for data-intensive and AI/ML applications. SUSE’s extensive partner ecosystem gives you tested, certified solutions for your specific requirements.

What are the key features of SUSE Linux Enterprise Server?

SUSE Linux Enterprise Server delivers several critical capabilities for demanding computational workloads. The platform supports multiple processor architectures, including x86-64 and Arm, along with specialized GPU and accelerator platforms. The system excels at hybrid cloud deployments, allowing seamless workload distribution between on-premises clusters and major cloud providers. Combined with simplified management tools and broad partner support, SLES provides a stable, certified foundation for your most intensive computational challenges.