Table of Contents
HPC in Industry: What Makes These Case Studies Special
In this chapter we look at how companies outside traditional academic research use HPC in day‑to‑day business. The goal is not to teach new technical mechanisms (those are covered in other chapters), but to show:
- What kinds of problems industry actually solves with HPC
- How workflows tend to look in practice
- What constraints matter in commercial settings (cost, time‑to‑market, regulation, risk)
We’ll use several concrete domains and focus on how HPC is used, what is different from typical “research” use, and what you should pay attention to if you want to work with HPC in industry.
We will cover five broad categories:
- Engineering and manufacturing
- Energy sector
- Finance and risk
- Media, entertainment, and digital services
- Pharma, biotech, and healthcare
Each case study is simplified, but representative of real practice.
1. Engineering and Manufacturing
1.1 Automotive crash simulation
Business goal: Reduce the number of physical crash tests while improving safety and shortening design cycles.
Typical workload:
- Large finite element models with $10^7$–$10^8$ elements
- Transient, nonlinear simulations with many small time steps
- Parameter sweeps (different impact speeds, angles, materials, design variants)
How HPC is used:
- Massive parametric studies:
Instead of one big run, an automotive company may launch hundreds of medium‑sized jobs, each representing a slightly different vehicle design or crash scenario. This uses the cluster as a throughput engine. - Tight coupling to CAD/PLM systems:
The CAD system exports geometry → meshing tools generate FE meshes → job scripts are generated automatically → submitted to the scheduler → results are pushed back into design databases. - Turnaround and scheduling constraints:
- Overnight runs are common: engineers expect results next morning.
- Projects often reserve “campaign windows” where certain models get priority on the cluster.
- License constraints: commercial CAE tools (e.g. LS-DYNA, Abaqus, Ansys) require expensive per‑core or per‑job licenses, so job size is often chosen based on available licenses, not only hardware.
Key HPC aspects in practice:
- Job size vs. license cost:
Running a job on twice as many cores may reduce time-to-solution, but may double the software license cost. Optimum parallelism is often a business decision, not a purely technical one. - Robust automation:
Fully automatic pipelines for pre‑processing, job submission, post‑processing, and report generation are critical. Manual steps are minimized to save engineering time. - Data handling:
Each crash simulation can produce tens to hundreds of GB of output. Companies use: - Tiered storage: fast parallel file systems for active runs, cheaper storage for older simulations.
- Aggressive downsampling: only key signals, not full fields, are stored long term.
1.2 Aerospace and turbomachinery: CFD at scale
Business goal: Improve fuel efficiency and reliability of aircraft components (e.g. wings, engines, turbines).
Typical workload:
- Computational fluid dynamics (CFD) with complex geometries
- Large meshes (billions of cells for high-fidelity cases)
- Combination of design optimization and few ultra‑high‑accuracy “hero runs”
How HPC is used:
- Design of Experiments (DoE) and optimization:
Many moderate‑resolution CFD simulations run in parallel to explore a design space (e.g. different wing shapes). An optimization algorithm coordinates them. - Hybrid parallelism:
Codes often use MPI across nodes and OpenMP (or GPU offload) inside nodes to reach good performance and efficient memory use. - Long‑running jobs and robustness:
- Single jobs may run for days or weeks.
- Checkpointing is mandatory to survive node failures or maintenance windows.
Key HPC aspects in practice:
- Verification and validation:
Industrial CFD results must match wind tunnel data and flight tests. Companies maintain: - Reference cases with known results
- Regression test suites that run on the cluster after code changes
- Certification and traceability:
For aerospace, regulatory agencies may require: - Documented software versions and compiler flags
- Reproducible input decks
- Detailed logs of simulation runs and any reruns
This directly links to reproducible workflows and environment management.
2. Energy Sector
2.1 Oil and gas: seismic imaging and reservoir simulation
Business goal: Locate resources, evaluate reservoirs, and plan extraction strategies with high economic return and controlled risk.
Seismic imaging
Workload characteristics:
- 3D wave propagation through large volumes of earth
- Very large datasets: TBs of input traces per survey
- Repeated full‑volume processing as better models or new data appear
How HPC is used:
- Highly parallel, often using domain decomposition over 3D volumes
- Heavy use of MPI and GPU accelerators in modern codes
- Strong emphasis on I/O performance, since raw seismic data is huge
Business constraints:
- Time criticality:
The value of information decreases as projects move on; faster processing can directly influence drilling decisions and investment timing. - Cost vs. quality trade‑offs:
Companies sometimes run a fast “preview” imaging on fewer nodes or lower resolution and then a high‑fidelity version when narrowing down prospects.
Reservoir simulation
Workload characteristics:
- Solving multiphase flow in porous media over long timescales
- Coupled physics (pressure, saturation, sometimes geomechanics)
- Uncertainty quantification: many simulations with varied parameters
How HPC is used:
- Running large ensembles of simulations to estimate risk distributions (e.g. probability of certain production outcomes)
- Coupled workflows where:
- Geologic models → reservoir simulators → economic models
all run on the cluster.
Key HPC aspects in practice:
- Ensemble workloads:
Thousands of similar simulations require: - Efficient job arrays
- Scripted workflow managers or commercial workflow engines
- Careful management of per‑run directories and outputs
- Data governance:
Many jurisdictions require strict handling of exploration data. HPC storage, access control, and audit trails are part of compliance.
2.2 Power grid and renewable energy planning
Business goal: Ensure reliable operation of power grids while integrating variable renewables (wind, solar) and planning future capacity.
Typical workload:
- Large graph‑based models of power networks
- Optimal power flow (OPF) and unit commitment problems
- Scenario analysis for different weather, demand profiles, or failures
How HPC is used:
- Large‑scale optimization problems solved with parallel solvers
- Many scenarios run in parallel for planning and risk assessment
- Integration with real‑time or near‑real‑time feeds (weather, load)
Business constraints:
- Operational deadlines:
Day‑ahead and intraday planning must complete within strict time windows; HPC capacity is sized so optimization always finishes in time. - Reliability:
System operators require highly reliable clusters and often operate in redundant data centers, sometimes with geographic separation.
3. Finance and Risk
3.1 Monte Carlo risk calculations
Business goal: Estimate risk metrics (e.g. Value‑at‑Risk, Expected Shortfall) for portfolios under many possible future scenarios.
Typical workload:
- Monte Carlo simulations with $10^5$–$10^8$ paths
- Complex instrument pricing models
- Daily, intraday, or even near‑real‑time runs
How HPC is used:
- Embarrassingly parallel tasks:
Each path or scenario is relatively independent, ideal for: - Distributing across many cores or nodes
- Using accelerators (GPUs) for massive throughput
- Job orchestration and priority:
Regulatory reports (e.g. end‑of‑day risk) often have fixed deadlines. Jobs may have increasing priority as deadlines approach.
Business constraints:
- Latency vs. cost:
Traders may want more frequent or larger simulations, but compute is expensive. Banks tune: - Number of scenarios
- Model detail
- Schedule (e.g. full run overnight, smaller runs intraday)
- Regulation and auditability:
Risk numbers are scrutinized by regulators: - All model versions, calibration data, and parameters must be auditable.
- HPC environments are tightly controlled; changes to software stacks are highly regulated.
3.2 High‑frequency and quantitative trading research
Business goal: Backtest trading strategies, calibrate models, and analyze time series for patterns, often on years of tick‑level data.
Typical workload:
- Large historical datasets (TBs of tick data)
- Many strategy variations and parameter sweeps
- Repeated backtesting over moving windows
How HPC is used:
- Data‑parallel processing over time ranges or instruments
- Large backtest batches run overnight or on weekends
- Integration with big data platforms for storage and preprocessing
Business constraints:
- Confidentiality and security:
Trading strategies and data are highly sensitive; HPC clusters are locked‑down environments with strict access controls. - Time‑to‑market:
The first firm to implement a profitable idea can gain advantage. Development workflows emphasize: - Rapid prototyping on a subset of data
- Scale‑out validation on the full cluster once promising results appear
4. Media, Entertainment, and Digital Services
4.1 Film and animation rendering
Business goal: Render high‑quality visual effects and animation within production schedules and budgets.
Typical workload:
- Rendering 3D scenes, often using path tracing or similar methods
- Millions of frames per feature film, with multiple render passes (lighting, shadows, etc.)
- Each frame is independent or nearly independent
How HPC is used:
- Render farms:
Clusters are used as specialized render farms: - Each job renders a frame or small batch of frames.
- The scheduler may be integrated with studio asset management and shot‑tracking tools.
- Heterogeneous workloads:
Some nodes may host GPUs for GPU‑accelerated renderers; others run CPU‑only jobs. The scheduler assigns jobs based on resource requirements.
Business constraints:
- Deadline‑driven usage:
Rendering demand spikes before major milestones (dailies, previews, final delivery). Clusters may run at near 100% utilization, 24/7. - Cost optimization:
Studios sometimes burst to cloud HPC resources to handle peak loads (e.g. final rendering weeks). They must: - Decide which shots can be safely offloaded
- Manage data transfer and encryption
- Track cloud costs per project
4.2 Online services and recommendation systems
Business goal: Improve user engagement and revenue through better recommendations, search results, or ads.
Typical workload:
- Training large machine learning models on logs of user interactions
- Frequent retraining on fresh data
- Hyperparameter tuning and A/B test analysis
How HPC is used:
- Clusters with GPUs or other accelerators for training deep learning models
- Parallel hyperparameter searches using job arrays or workflow systems
- Integration with data warehouses and streaming platforms
Business constraints:
- Freshness of models:
Recommendation quality can degrade if models are not retrained regularly. HPC capacity is sized so retraining fits within a defined time window (e.g. nightly). - Production integration:
Models trained on the cluster must be exportable, versioned, and deployable in low‑latency production systems. This requires consistent environments and reliable artifact management.
5. Pharma, Biotech, and Healthcare
5.1 Drug discovery: virtual screening and molecular simulation
Business goal: Identify promising drug candidates faster and at lower cost, before expensive lab and clinical trials.
Virtual screening
Workload characteristics:
- Docking millions to billions of candidate molecules against a target protein
- Each docking calculation is relatively small and independent
How HPC is used:
- Massive job arrays; thousands of jobs running in parallel
- Often GPU‑accelerated for scoring or simulation
- Workflow tools to manage compound libraries, input preparation, and result filtering
Business constraints:
- Prioritization:
After initial large‑scale screening, a smaller set of promising compounds undergo more expensive, high‑fidelity simulations. The cluster must support both high‑throughput and high‑fidelity jobs. - Intellectual property:
Compound libraries and targets are highly sensitive; strict access control, encryption, and audit trails are required.
Molecular dynamics (MD) simulations
Workload characteristics:
- Atomistic simulations of proteins, membranes, complexes
- Jobs can run for microseconds of simulated time, requiring long wall‑clock times
How HPC is used:
- Parallel MD codes with careful GPU and CPU utilization
- Multiple replicas or conditions run in parallel (temperature/pressure variations, mutations, different ligands)
Key HPC aspects in practice:
- Reproducibility and provenance:
Scientific results feed directly into regulatory submissions and patents. Simulation conditions, seeds, and parameters must be logged carefully. - Cross‑disciplinary workflows:
Chemists, biologists, and computational scientists share the cluster. User interfaces can range from simple command line to web front‑ends that submit jobs behind the scenes.
5.2 Medical imaging and clinical decision support
Business goal: Improve diagnostics and treatment planning using large‑scale image analysis and predictive models.
Typical workload:
- Processing 3D or 4D imaging data (CT, MRI, PET)
- Training and running deep learning models on images and clinical data
- Running population‑level analyses for research or quality control
How HPC is used:
- GPU nodes for image segmentation, detection, or reconstruction algorithms
- Batch processing of large image datasets
- Sometimes near‑real‑time or interactive use in planning (e.g. radiotherapy dose calculations)
Business constraints:
- Privacy and regulation:
Healthcare data is heavily regulated. HPC systems handling such data must: - Enforce strict access control
- Support anonymization and de‑identification workflows
- Comply with standards (e.g. HIPAA in the US, GDPR in the EU)
- Reliability and validation:
Models affecting clinical decisions need rigorous validation and version control. Reproducible environments and deterministic workflows are not optional.
6. Cross‑Cutting Themes from Industry Case Studies
Across these diverse industries, several common patterns emerge in how HPC is actually used:
6.1 Throughput vs. single‑job performance
- Many industrial users care more about total daily throughput than the peak speed of one job.
- Clusters are tuned to:
- Run large numbers of similar jobs efficiently
- Minimize queue times for priority workloads
- Balance different project needs
6.2 Cost, licensing, and business optimization
- Decisions about:
- Parallelism level
- Use of GPUs
- Job sizing
are often driven by cost models (hardware, energy, support, software licenses), not only by technical performance. - Companies maintain internal “cost per simulation” metrics and relate them to business value (e.g. cost per crash scenario vs. saving one physical test).
6.3 Automation and workflow orchestration
- Industry HPC environments often build or adopt workflow tools that:
- Connect business systems (PLM, trading platforms, lab systems) to the cluster
- Automatically stage input data and manage output
- Handle job dependencies, retries, and notifications
- This reduces manual error and makes results more traceable and reproducible.
6.4 Regulatory and compliance requirements
- In many industries (aerospace, finance, energy, healthcare, pharma), simulations contribute to:
- Regulatory filings
- Safety cases
- Audit trails
- HPC centers therefore implement:
- Strict change management for software and compilers
- Environment versioning
- Detailed logging of who ran what, when, with which inputs
6.5 Security and data governance
- Sensitive data (financial, medical, proprietary designs) requires:
- Network segmentation
- Role‑based access control
- Encryption for data in transit and at rest
- Controlled remote access to login nodes
- This shapes how users interact with the system, often with more restricted environments than in open academic clusters.
7. What This Means for You as an HPC Practitioner
When moving from academic or training contexts into industry:
- Expect business goals (time‑to‑market, cost, risk, compliance) to strongly influence how HPC is configured and used.
- Learn to think in terms of workflows and automation, not just single runs.
- Pay attention to traceability, reproducibility, and security—they are central in many industrial environments.
- Realize that in many roles, your impact is measured not just by code speed, but by:
- How much engineering time you save
- How much more insight you enable per unit of compute
- How reliably the system runs day after day
These case studies illustrate that HPC in industry is less about individual “hero simulations” and more about building reliable computational engines that support critical business decisions.