Ensemble design within budgets
The number of runs, resolution and scenarios are traded off against available compute, with the effect on uncertainty estimates stated.
GCRI · Systems Science and Modelling
Computing for modelling
High-performance, cloud and edge computing determine which models can be run, how many ensemble members and scenarios are feasible, and whether results can be reproduced. In this field computing is planned alongside modelling, not after it.
01Register
Computing for modelling on the record.
Method recordSTEM method
- MethodHigh-performance, cloud and edge computing
- StewardData and Compute
- Applied inSystems Science and Modelling
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What high-performance, cloud and edge computing does here.
High-performance, cloud and edge computing provide the processing and storage capacity for simulation, analytics and digital services. They are both an enabler of risk analysis and a critical dependency for society. In methods and modelling, risk management and systemic risk and research and evidence, computing capacity determines how fast and at what resolution risks can be modelled. Its concentration is itself a systemic dependency for finance, health and government services. The canonical description is on the STEM Commons.
Workflow orchestration
Coupled models and ensembles are run through workflow systems that record provenance and recover from failures.
Reproducible environments
Model environments are captured in containers so that runs can be repeated on different systems.
03Monitor
Compute workload.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Compute workload
Systems science · Computing for modelling · Run R-1062
- Channels reporting89of 100Normal
- Median latency182ms
- Records failing checks0.7per 1,000Watch
- Backlog24min
Gaps on 2 channelsGCRI · Systems Science and Modelling
Latency, p95
Events
- 06:16WarnChannel C-26 stale
- 07:20InfoSchema v4 accepted
- 08:47ErrorChecksum mismatch, batch quarantined
- 08:20DoneReplay complete
04Data
What the method consumes.
Datasets and records used by high-performance, cloud and edge computing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Compute allocations | Budgets | Published statistics |
| Workflow logs | Provenance | OBSERVED |
| Container images | Environments | DECLARED |
05Methods and evidence
How computing for modelling 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.
Model, equations and assumptions
The model structure used for computing for modelling, the assumptions it makes and how each is tested.
Risk of element i
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityExpected annual loss
EAL = ∑k pk · Lkover events k with annual probability pkCoupled systems
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
| Check | How it is done |
|---|---|
| Cross-platform reruns | Models are rerun on another system to confirm results |
| Resource accounting | Compute used per experiment is recorded |
| Failure recovery testing | Workflows are tested for recovery from interrupted runs |
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
07Process
How the process runs.
Process
Computing for modelling: Build, sign and accept
An admitted mission becomes a cycle project; the build is pinned, verified and signed, and the Global Risks Forum (GRF) records acceptance for exercise, not permission to deploy.
01 Request and sources
- 01Admitted mission becomes a cycle projectRecord · Universe to FoundryAn admitted mission becomes a cycle project with a decision case.
- 02Qualify data sources and resourcesStep · the Global Centre for Risk and Innovation (GCRI)GCRI qualifies data sources and resource offers and builds the scenario models. An offer is not an allocation.
02 Build and verify
- 03Source snapshotRecord · NXS-FAC (blueprints, builds and signing)The snapshot pins the commit, dependencies and generator versions.
- 04Build contractRecord · NXS-FAC (blueprints, builds and signing)The build contract declares the artefact kind, environment and reproducibility envelope.
- 05Integrate and buildModel · GCRIGCRI integrates software and data, signs builds, and tests security and interoperability.
- 06Verification suiteGate · NXS-FAC (blueprints, builds and signing)Required and advisory checks stay separate. Reproducibility needs pinned inputs, declared nondeterminism and independent rebuilds.
03 Sign and admit
- 07Sign the exact artefactStep · NXS-FAC (blueprints, builds and signing)NXS-FAC builds, attests and requests a signature via a scoped signer identity. It holds no key material.
- 08Package admissionStep · NXS-NXQ (software, models and capability lifecycle)NXS-NXQ receives the signed package and keeps admission evidence that binds the task, data class, evaluation suite and expiration.
04 Accept
- 09Core Build ReportRecord · GCRIThe Core Build Report is the go or no-go record. Universe assembles the Core and holds the decision for the exercise.
- 10G3 · Build accepted for exerciseGate · The Global Risks ForumGRF records whether the build is accepted for exercise. A signed build or release candidate is not permission to deploy to production.
The programme year · NXS-FAC · UniverseDesigned programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The record order follows the Builds and Releases room lists of NXS-FAC.
09Stewardship
Who maintains the method.
Data and Compute stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of high-performance, cloud and edge computing in this field?
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
How are results checked?
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Systems Science and Modelling
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