Table of Contents
Scientific Research and Discovery
High-performance computing underpins much of modern science. Many questions are simply too complex for pen-and-paper math or small desktop simulations.
Climate and weather modeling
- Global climate models (GCMs) simulate the atmosphere, oceans, ice, and land over decades to centuries.
- Millions to billions of grid cells represent different parts of the Earth.
- Time steps are often seconds or minutes, run forward for simulated years.
- Weather prediction:
- National centers (e.g., ECMWF, NOAA, Met Office) run daily forecasts on some of the world’s largest supercomputers.
- Higher resolution (smaller grid cells) improves forecasts but dramatically increases computation.
- Typical uses:
- Seasonal forecasts (e.g., El Niño).
- Extreme event prediction: hurricanes, heatwaves, floods.
- Studying impacts of possible CO₂ emission scenarios.
Astrophysics and cosmology
- Cosmological simulations:
- Track the formation of structure in the universe: galaxies, clusters, dark matter halos.
- Evolve billions of particles representing dark matter and gas over billions of years of cosmic time.
- Stellar and supernova simulations:
- Model the life cycle of stars and the physics of supernova explosions.
- Require solving complex fluid dynamics and nuclear reaction networks.
- Outcomes:
- Testing theories of dark matter and dark energy.
- Interpreting observations from telescopes and space missions.
Computational chemistry and materials science
- Molecular dynamics (MD):
- Simulate motion of atoms and molecules over time.
- Used to study protein folding, ligand binding, membrane behavior, and materials at the atomic scale.
- Quantum chemistry and electronic-structure calculations:
- Methods like DFT (Density Functional Theory) used to compute electronic properties.
- Help design new catalysts, battery materials, semiconductors.
- Applications:
- Drug discovery: screening how molecules bind to target proteins.
- Materials design: high-temperature alloys, photovoltaic materials, solid electrolytes.
High-energy and nuclear physics
- Lattice QCD (Quantum Chromodynamics):
- Discretizes space-time on a 4D lattice to study strong nuclear forces.
- Extremely computationally intensive, among the “flagship” users of top supercomputers.
- Nuclear reactor and radiation transport simulations:
- Model neutron transport, fuel behavior, and safety margins in reactors.
- Support reactor design and safety analysis without solely relying on experiments.
Engineering and Industry Applications
HPC has become core infrastructure in many engineering and industrial workflows, replacing or complementing physical prototyping.
Aerospace and automotive design
- Computational Fluid Dynamics (CFD):
- Simulates airflow over wings, fuselages, car bodies, and turbine blades.
- Used to reduce drag, improve lift, optimize cooling and combustion.
- Structural mechanics and crash simulations:
- Finite element analysis (FEA) models stresses and deformations under load, impact, or vibration.
- Critical for designing safer cars, aircraft, and spacecraft.
- Benefits:
- Faster design cycles: fewer wind-tunnel tests and physical prototypes.
- Cost savings and exploration of many design variants via parameter sweeps.
Energy sector: oil, gas, renewables, and nuclear
- Seismic imaging and reservoir simulation:
- Process huge datasets from seismic surveys to image subsurface geology.
- Model fluid flow in reservoirs to optimize well placement and production.
- Wind and solar:
- CFD for wind farm layout and turbine placement.
- Modeling of atmospheric turbulence, wake interactions, and energy yield.
- Nuclear engineering:
- Reactor core simulations, fuel performance, accident scenario analysis.
- HPC impact:
- More efficient resource use, improved safety, and lower exploration risk.
Manufacturing and industrial optimization
- Process simulation:
- Chemical plants: simulate reactors, distillation columns, and heat exchangers.
- Steel, glass, and plastics: model casting, forming, and cooling processes.
- Topology and design optimization:
- Automatically “evolve” shapes and structures under constraints to minimize weight or materials while maintaining strength.
- Supply-chain and logistics optimization:
- Large-scale mixed-integer optimization for production scheduling, routing, and inventory management.
Medicine, Health, and Life Sciences
HPC is increasingly central to healthcare and biomedical research.
Genomics and bioinformatics
- Genome sequencing and assembly:
- Analyze terabytes of sequencing data to reconstruct genomes.
- Used in research, clinical diagnostics, and epidemiology.
- Population-scale genomics:
- Compare thousands to millions of genomes to identify disease-linked variants.
- Applications:
- Personalized medicine, rare disease diagnosis, cancer genomics, tracking pathogen evolution (e.g., during outbreaks).
Medical imaging and image analysis
- 3D/4D image reconstruction:
- CT, MRI, PET scans often require significant compute for high-quality reconstruction and noise reduction.
- Image-based diagnostics and segmentation:
- HPC-accelerated machine learning to automatically detect tumors, lesions, or anatomical structures.
- Clinical impact:
- Faster and more precise imaging, enabling better diagnosis and treatment planning.
Computational physiology and personalized medicine
- Digital twins of organs or patients:
- Heart simulations: model electrical activity and blood flow to guide surgery or treatment.
- Brain simulations: study epilepsy, stroke, or neurodegenerative diseases.
- Drug response modeling:
- Predict how a particular patient might respond to specific drugs or treatment regimes.
- Potential outcomes:
- More effective therapies with fewer side effects.
- Reduced need for invasive procedures.
Finance, Economics, and Risk Management
Many financial and economic problems involve enormous numbers of scenarios and constraints that are well-suited to HPC.
Quantitative finance and risk analysis
- Monte Carlo simulations:
- Used for pricing complex derivatives and assessing portfolio risk under many possible market evolutions.
- Require simulating millions of paths with correlated random variables.
- Stress testing and regulatory compliance:
- Large financial institutions run scenario analyses mandated by regulators, often with tight deadlines.
- Benefits:
- Better risk management and more accurate pricing under uncertainty.
Algorithmic trading and market simulation
- Backtesting:
- Evaluate trading strategies over years of historical tick-level data.
- Market microstructure modeling:
- Simulate order books and trader interactions to study liquidity and price impact.
- While ultra-low-latency trading itself may depend more on specialized hardware, HPC clusters are used to design and test the strategies.
Artificial Intelligence, Data Analytics, and Big Data
Modern AI and large-scale analytics are effectively a major branch of HPC.
Training large AI and machine learning models
- Deep learning:
- Training large neural networks (e.g., language models, vision models) on massive datasets.
- Uses GPUs and specialized accelerators on HPC-style infrastructure.
- Distributed training:
- Parallelizes both data and model across many nodes and devices.
- Applications:
- Natural language processing, computer vision, scientific ML (e.g., surrogate models for simulations).
Large-scale data analytics
- Data mining and clustering:
- Discovering patterns in very large datasets from sensors, logs, or user behavior.
- Graph and network analysis:
- Social networks, biological networks, transport networks.
- Examples:
- Anomaly detection in cybersecurity.
- Recommender systems for e-commerce and streaming services.
Public Policy, Safety, and Society
HPC plays a role in policy, safety, and planning that affects large populations.
Natural hazards and disaster modeling
- Earthquake and tsunami simulations:
- Model wave propagation and ground motion to inform building codes and evacuation plans.
- Flood and wildfire modeling:
- Predict fire spread or river flooding based on weather, terrain, and vegetation.
- Usage:
- Early warning systems, risk maps, emergency response planning.
Urban planning and transportation
- Traffic and transport simulations:
- Model traffic flow at city or regional scales to evaluate infrastructure changes.
- Evacuation planning:
- Simulate crowd and vehicle movement in emergencies.
- Benefits:
- More efficient and safer cities with better use of infrastructure.
National Security and Defense
Many of the earliest supercomputers were built for national security purposes and remain in use today for this domain.
Weapons and defense systems simulation
- Nuclear stockpile stewardship:
- Simulate nuclear weapons behavior without live testing.
- Requires detailed multiphysics simulations (hydrodynamics, radiation transport, materials).
- Ballistics and defense systems:
- Modeling trajectories, interception scenarios, and system performance under extreme conditions.
Cryptography and cybersecurity
- Cryptanalysis:
- Testing and evaluating the strength of cryptographic systems.
- Cybersecurity analytics:
- Large-scale log analysis, intrusion detection, and anomaly detection with AI and graph analysis techniques.
Grand Challenge Problems and Global Initiatives
Some HPC efforts are organized as “grand challenges” that span multiple disciplines and countries.
Exascale and flagship science campaigns
- Exascale projects:
- National and international programs to build and exploit machines capable of $10^{18}$ floating-point operations per second.
- Example application domains often targeted:
- Climate and Earth system science.
- Fusion energy modeling.
- Precision medicine and cancer research.
- Advanced manufacturing and materials discovery.
Community codes and open science
- Many HPC applications are implemented as large, shared “community codes”:
- Climate: atmosphere–ocean models used by multiple research groups.
- Astrophysics: simulation frameworks for galaxies or stars.
- Chemistry: widely used MD and quantum chemistry packages.
- These codes:
- Run on major national or regional supercomputers.
- Enable reproducible science and collaboration across institutions.
How These Examples Shape This Course
Across all these domains, you can see recurring patterns:
- Problems are too large or too slow for a single desktop or laptop.
- We need:
- Many cores, nodes, and accelerators working together.
- Efficient use of memory, storage, and interconnects.
- Specialized software stacks and programming models.
The rest of this course will focus on the concepts, tools, and practices that make such applications possible on modern HPC systems, independent of the specific scientific or industrial field.