GCRI · Data and Compute
High performance computing
High-performance computing runs the weather forecasts, climate projections, engineering simulations, genomics and AI training that modern science and public services rely on. National capacity depends on machines, software, skilled people, energy and long-term funding. This page sets out how HPC capacity is built and used, how it is measured, and what evidence serves the research funders and agencies that plan national computing.
High performance computing on the record.
High performance computing: how the risk develops.
HPC/GPU workloads and scientific compute. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Capability and capacity differ
Some problems need one very large machine running one job; others need many smaller jobs. Planning for one does not serve the other.
Machines age fast
Supercomputers are replaced every few years as performance per watt improves. Gaps between systems interrupt the work of forecasting and research centres.
Software and skills limit use
Codes must be ported and optimised for new architectures such as GPUs. Shortages of research software engineers leave capacity underused.
Power sets the ceiling
Large systems draw megawatts. Energy cost and grid capacity increasingly decide where and how large new machines can be.
Connected activity in the contiguous United States.
Night lights, population density and true-colour imagery over the contiguous United States, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Connected activity, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on the contiguous United States.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
High-performance computing runs the models every domain relies on.
Capacity studies are designed for the Strategic Systems series. The TOP500 and Green500 lists record benchmark performance and efficiency of large systems; benchmarks do not measure scientific usefulness.
- Compute access policies of research centres.
- Model performance studies.
Model high performance computing as a system. Run the cascade.
Assembled from the Nexus Standards area profile Digital, telecom and platforms: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-ice loss
- Coastal erosion (set off downstream)
- Biodiversity loss (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
- Digital, telecom and platforms
- Energy utilities and grids Coupling
- Media and information Coupling
- Finance, insurance and capital markets Coupling
- Defence and civil protection Coupling
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-T8Software and supply-chain integrity riskNRS-X6Cyber and digital security risk
Observation
- ITU DataHub ICT indicators (listed for reference)
- World Development Indicators (listed for reference)
- WMO Alert Hub and Register of Alerting Authorities (listed for reference)
- Proportion of population covered by a mobile network, by technology
- Fixed broadband subscriptions per 100 inhabitants, by speed
- Individuals using the Internet (% of population)
- Number of people per 100,000 that are covered by early warning information through local governments or through national dissemination mechanisms
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Sustained performance on standard benchmarks | floating point operations per second | twice yearly | TOP500 list | OBSERVED |
| Energy efficiency | floating point operations per watt | twice yearly | Green500 list | OBSERVED |
| System utilisation | percent of available node hours used | monthly | Centre usage records | OBSERVED |
| Allocation oversubscription | ratio of requested to available hours | per call | Allocation records | Published statistics |
| Research software engineers employed | count | annual | Centre staffing records | REPORTED |
| Codes ported to accelerators | percent of major community codes | per assessment | Centre reports | REPORTED |
How high performance computing is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Datasets and provenance
The data classes behind high performance computing and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| TOP500 and Green500 lists | Performance and energy efficiency of the most powerful systems | Public, TOP500 terms |
| EuroHPC Joint Undertaking reports and access calls | European supercomputers and allocation | Public, EU terms |
| National HPC centre annual reports | Usage, allocation and science outputs | Public records, terms per centre |
| ESFRI roadmap and landscape analysis | European research infrastructure planning | Public, EU terms |
| WMO reports on numerical weather prediction capacity | Computing used by meteorological services | Public, WMO terms |
Who decides, and what the evidence serves.
Data and Compute prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Research funders and science ministries | What computing capacity to fund and where | Capacity and demand reviews; oversubscription |
| HPC centres | How to allocate time and support users | Utilisation and allocation analyses |
| Meteorological and climate services | When to procure the next system | Performance and workload evidence |
Outputs this topic produces
- National HPC capacity and demand reviews
- Allocation and utilisation analyses
- Workforce and software readiness assessments
- Evidence notes for computing investment plans
What HPC experts ask.
- Which workloads need tightly coupled supercomputers, and which can run on cloud or distributed resources?
- How should capacity be balanced between traditional simulation and AI workloads, which favour different architectures?
- Can scientific codes be ported to new architectures without years of rewriting?
- Who gets access, and how are competing scientific priorities weighed?
What makes HPC distinct.
Large simulations divide a problem into many pieces that must exchange data constantly. Performance depends not only on processor speed but on memory bandwidth, the interconnect between nodes and input and output to storage. A system that is fast on paper can be slow for a given code if these are unbalanced.
Accelerators such as GPUs deliver much more arithmetic per unit of energy for suitable workloads, which is why AI training uses them. But many scientific codes were written for conventional processors and need substantial effort to use accelerators well. Architecture shifts therefore create a software burden and a skills burden, not just a procurement decision.
How capacity is planned and evaluated.
- 01
Workload analysis
Profiling the actual codes and data volumes a community runs. Limits: future workloads, especially AI, are hard to predict.
- 02
Benchmarks
Standard tests of computing performance. Limits: no benchmark represents all workloads, and peak figures overstate real performance.
- 03
Capacity planning
Modelling demand, utilisation and replacement cycles with optimisation and operations research. Limits: depends on funding cycles and hardware availability.
- 04
Energy efficiency analysis
Measuring useful work per unit of energy, linked to power systems engineering. Limits: comparisons across workloads are difficult.
How it is studied, and what the methods cannot say.
- 01High-performance, cloud and edge computingWorkload characterisation · Porting codes to new architectures · Hybrid cloud and HPC workflowsApplied here
- 02Energy generation, storage and grid technologyCooling technology comparison · On-site energy options · Heat reuse feasibilityApplied here
- 03Optimisation and operations researchJob scheduling policies · Carbon-aware workload shifting · Capacity planningApplied here
- 04Physics and quantum scienceEnergy limits of computation · Thermal management physics · Assessing new computing paradigmsApplied here
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is a supercomputer?
What does the TOP500 measure?
Why are GPUs used in HPC?
Will cloud computing replace supercomputers?
Why do AI and simulation compete for capacity?
Does the platform publish rankings of computing capacity?
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