Examples of real-world HPC applications

Grand Scientific Challenges

High performance computing is used whenever a question or design is too big, too detailed, or too time‑critical for ordinary computers. Instead of staying abstract, it is useful to see how this plays out in real projects.

One of the most visible areas is climate and weather. Modern global climate models divide the Earth into a three‑dimensional grid in space and step forward in time, solving systems of equations that describe the atmosphere, oceans, and ice. A typical climate run might simulate hundreds of years of Earth time, using billions of grid cells and many physical processes such as radiation, clouds, and chemistry. Production climate simulations often run on tens of thousands of CPU cores for days, and still scientists must make approximations and choices about resolution to keep the problem tractable. Short‑range and medium‑range weather forecasts use similar techniques with higher spatial and temporal resolution on specific regions, which brings even greater computational cost but must be delivered within strict deadlines. The quality and timeliness of forecasts depends directly on the available computing power.

Astrophysics and cosmology provide another class of grand challenge. Simulations of the formation of galaxies and the large‑scale structure of the universe track the motion and interaction of many millions or billions of particles, representing dark matter and gas. The gravitational forces between all these particles are computed repeatedly as the simulation advances. Directly computing the force between every pair of particles would require a number of operations that grows like $N^2$ with the number of particles $N$, so researchers use advanced algorithms to reduce this cost, but the total work is still enormous. Large cosmological simulations can consume millions of core hours on top supercomputers and produce petabytes of data. Comparing these simulated universes with telescope observations helps refine models of dark matter, dark energy, and galaxy formation.

Engineering and Industrial Design

Beyond fundamental science, HPC is deeply embedded in engineering and industrial workflows. One important example is computational fluid dynamics, often abbreviated as CFD. In CFD, engineers simulate the flow of air, water, or other fluids around objects. This is used to design aircraft wings, car bodies, wind turbine blades, and even racing yachts. The equations that govern fluid flow are solved on fine meshes that describe complex geometries. To capture turbulent flows accurately, simulations must use very small time steps and very dense meshes, which leads to extremely large numbers of unknowns and updates per time step. An automotive company might use HPC to run many CFD simulations in parallel, each with slightly different shapes or conditions, to optimize aerodynamic performance and reduce fuel consumption.

Structural mechanics and crash simulations are also major HPC applications in industry. Auto manufacturers simulate full vehicle crashes instead of performing only physical crash tests. In such simulations, the car is represented as a detailed assembly of many parts, each with material properties, joints, and deformation behavior. During a simulated crash, the code must compute contact forces, material failure, and large deformations over a very short physical time scale, but with fine numerical resolution. A single high‑fidelity crash simulation can use thousands of cores for many hours. By sweeping many impact speeds, angles, and designs, engineers can improve safety and reduce development costs.

In civil and mechanical engineering, finite element simulations are used to design bridges, skyscrapers, turbines, and manufacturing equipment. Large models with millions or billions of elements are solved so that stresses and displacements are known throughout the structure under many load scenarios. Companies rely on HPC to evaluate many design variations, support digital twins of assets in operation, and perform reliability analyses that require running the same model thousands of times with different input parameters.

Medicine, Biology, and Health

HPC has become central to life sciences and healthcare. One key application is molecular dynamics, where the motion of atoms in molecules and materials is simulated at very small time scales. A typical molecular dynamics simulation might follow a protein in water with hundreds of thousands or millions of atoms, using time steps on the order of femtoseconds. To simulate even microseconds of physical time, the program must perform trillions of force evaluations and updates. Supercomputers and GPU‑accelerated systems are used to simulate protein folding, enzyme function, drug binding, and membrane transport, all of which contribute to rational drug design and basic biological understanding.

Another major area is bioinformatics and genomics. Sequencing machines produce massive amounts of genetic data. HPC clusters are used to assemble genomes from fragments, align sequences to reference genomes, and search for mutations across large populations. Tasks like read alignment, variant calling, and de novo assembly involve trillions of comparisons and many passes through large data sets. National and international projects that study the genomes of thousands or millions of individuals rely on large HPC infrastructures so that analyses complete in reasonable times and can be updated as methods improve.

In medical imaging and treatment planning, HPC appears both in data processing and in physics‑based simulation. High resolution CT, MRI, and PET scans require reconstruction algorithms that are computationally intensive, especially when advanced methods or real‑time processing is desired. In radiation therapy for cancer treatment, planning systems simulate the transport and absorption of radiation in the patient’s body to determine safe and effective dose distributions. These simulations must run fast enough to fit within clinical workflows and must be accurate enough to trust clinically. HPC shortens planning times and enables more sophisticated protocols, such as adaptive therapy that updates plans as the patient’s anatomy changes.

Energy, Earth, and the Environment

Energy production, resource exploration, and environmental protection are all powered by HPC. In the oil and gas industry as well as geothermal and carbon storage projects, seismic imaging and inversion are central. Companies fire acoustic waves into the ground and record reflected signals with large arrays of sensors. To build a picture of the subsurface, they simulate wave propagation through candidate earth models and compare predictions with observed signals. Many such simulations are needed to adjust the model until it matches measurements well. These wave simulations are run on very large computational grids, and each iteration can require thousands of nodes, making the full inversion process a classic HPC workload.

In nuclear energy and fusion research, HPC is used to study plasma behavior, reactor materials, and safety scenarios. Fusion devices like tokamaks contain hot plasmas with complex magnetic field configurations. Simulations must account for multiple physical processes, from turbulence to particle transport, over a wide range of spatial and temporal scales. Running these coupled models would be impossible on ordinary computers, but on supercomputers researchers can explore many operational regimes and control strategies.

Hydrology, ocean modeling, and environmental risk assessment also depend heavily on HPC. Flood prediction models simulate rainfall, river flows, and coastal interactions under different weather and infrastructure conditions. Combined with terrain data, they help planners design flood defenses, evacuation routes, and zoning policies. Large water resource systems, including dams, irrigation networks, and groundwater reservoirs, are analyzed through many scenario simulations that take into account climate variability and human usage. Here the value of HPC is not just running one big model, but running many variants in ensembles that capture uncertainty.

Finance, Business, and Data Analytics

Outside the traditional scientific and engineering domains, HPC also plays an important role in finance and broader data analytics. In quantitative finance, risk estimation, option pricing, and portfolio optimization often involve models that must be evaluated for very many possible future scenarios. Monte Carlo methods are widely used, where the same model is run under randomly sampled market conditions to estimate distributions of profits and losses. The number of scenarios $N$ required for accurate statistics can be very large, and computing each scenario can be expensive, so financial institutions use HPC clusters to run these simulations in parallel and meet regulatory reporting deadlines.

Large companies analyze customer behavior, supply chains, and logistics using techniques borrowed from both HPC and big data systems. For some analytics tasks, such as large scale optimization, recommendation engines, or simulation based planning, traditional cluster frameworks are not sufficient and specialized HPC techniques and hardware become valuable. As data volumes increase, there is growing overlap between high performance computing and data intensive computing.

Artificial Intelligence and Machine Learning

Modern artificial intelligence relies heavily on HPC style infrastructure. Training large neural networks involves repeated passes over data sets that can be terabytes in size, combined with very large numbers of floating point operations. For example, training a deep model can require many matrix multiplications, convolutions, and gradient updates. These operations map well to GPUs and other accelerators that are common in HPC environments. Large language models and image recognition systems are typically trained on specialized clusters with thousands of GPUs, interconnected by high bandwidth networks, and orchestrated by parallel training software.

The connection between AI and HPC is two‑way. On one side, AI workloads use supercomputers to train ever larger models. On the other side, traditional HPC applications increasingly incorporate machine learning models to accelerate simulations, approximate expensive calculations, or analyze outputs. For example, a climate model might use a trained neural network to approximate a small physical process that would otherwise be very costly to resolve directly, which reduces the total simulation cost.

National Security and Public Policy

Some of the earliest and most demanding applications of HPC have arisen from national security needs. Nuclear weapons research, for instance, relies on large scale simulations to understand weapon behavior, maintain stockpile safety without explosive testing, and model physical processes like hydrodynamics and radiation transport. These simulations remain among the most computationally intensive tasks and often drive the development of new supercomputing systems.

In a broader public policy context, governments use HPC for epidemic modeling, transport planning, and disaster response. For disease spread, models that represent populations and their interactions can simulate the effects of interventions such as vaccination, movement restrictions, or school closures. During fast moving crises, policymakers need up to date projections under many possible strategies. Running these scenario ensembles in time for decision making is only possible with significant computing resources.

HPC is also used for simulating infrastructure networks such as power grids and communication systems. Large interconnected grids are modeled to assess stability under failures, demand peaks, or the addition of renewable energy sources. These simulations involve solving large sets of equations and running many variations, which is a natural fit for HPC clusters.

Everyday Impact of HPC

Although many HPC applications seem distant or specialized, their outcomes influence daily life. Weather forecasts on phones, safer cars, cheaper flights, reliable power grids, and modern medicines are all linked to simulations and data analysis carried out on large computing systems. Often, HPC is part of the development and planning stage rather than the final product, so it is not visible to end users.

For absolute beginners, the important point is that HPC is not tied to a single field or type of program. Any domain with large models, large data, or many scenarios to explore can benefit. As you progress through this course, the concrete examples from climate, engineering, health, energy, finance, and AI can serve as reference points for understanding why performance matters and how parallel computing techniques are put to work in practice.