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FastEddy

Leverage fast, energy-efficient GPU computing to expand LES use in research and enable microscale and multiscale turbulence-resolving boundary-layer modeling for local weather prediction and practical science and engineering applications.
https://github.com/ncar/fasteddy-model

Category: Atmosphere
Sub Category: Atmospheric Composition and Dynamics

Last synced: about 19 hours ago
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Repository metadata

An NSF NCAR developed, parallelized and GPU-resident, large-eddy simulation code for accelerated modeling of the atmospheric boundary layer.

README.md

FastEddy®

©2016 University Corporation for Atmospheric Research

DOI

Open-source License

The FastEddy® model is licensed under the Apache License, Version 2.0 (the "License");
you may not use any source code in this repository except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Description

FastEddy® (FE) is a large-eddy simulation (LES) model developed by the Research Applications Laboratory (RAL) at the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) in Boulder, Colorado, USA. The fundamental premise of FastEddy model development is to leverage the accelerated and more power efficient computing capacity of graphics processing units (GPU)s to enable not only more widespread use of LES in research activities but also to pursue the adoption of microscale and multiscale, turbulence-resolving, atmospheric boundary layer modeling into local scale weather prediction or actionable science and engineering applications.

Contact

Please submit all comments, feedback, suggestions, or questions by email to the NSF NCAR FastEddy team at fasteddy@ucar.edu. Further information about FastEddy applications and research is available via the RAL website.

Citation

FastEddy should be cited as follows:

Sauer, J., and D. Muñoz-Esparza. "The FastEddy resident-GPU accelerated large-eddy
simulation framework: model formulation, dynamical-core validation and performance
benchmarks". Journal of Advances in Modeling Earth Systems, vol. 12 (2020)
https://doi.org/10.1029/2020MS002100

Documentation

FastEddy documentation for this version and previous versions are available through Read the Docs.

Tutorials

FastEddy tutorials for idealized cases are available in the Tutorials section of the documentation.

Publications

FastEddy publications are available in the Publications section of the documentation.

Owner metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 4 days ago

Total Commits: 73
Total Committers: 4
Avg Commits per committer: 18.25
Development Distribution Score (DDS): 0.425

Commits in past year: 18
Committers in past year: 3
Avg Commits per committer in past year: 6.0
Development Distribution Score (DDS) in past year: 0.389

Name Email Commits
Julie Prestopnik j****o@u****u 42
Jeremy Sauer j****r@u****u 17
Domingo Muñoz-Esparza d****m@u****u 13
Joe Schoonover 1****e 1

Committer domains:

Issue and Pull Request metadata

Last synced: 18 days ago

Total issues: 39
Total pull requests: 82
Average time to close issues: 28 days
Average time to close pull requests: 2 days
Total issue authors: 5
Total pull request authors: 6
Average comments per issue: 0.18
Average comments per pull request: 0.33
Merged pull request: 68
Bot issues: 0
Bot pull requests: 0

Past year issues: 9
Past year pull requests: 19
Past year average time to close issues: 4 days
Past year average time to close pull requests: about 9 hours
Past year issue authors: 3
Past year pull request authors: 4
Past year average comments per issue: 0.0
Past year average comments per pull request: 0.05
Past year merged pull request: 14
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/ncar/fasteddy-model

Top Issue Authors

  • jprestop (18)
  • jsauer-NCAR (11)
  • domingom (8)
  • sebastipa (1)
  • AkshayPatil1994 (1)

Top Pull Request Authors

  • jprestop (41)
  • domingom (23)
  • jsauer-NCAR (11)
  • fluidnumerics-joe (4)
  • jeffhancockNCAR (2)
  • MrCroxx (1)

Top Issue Labels

  • pillar: community (26)
  • requestor: Multiscale NWP Team (23)
  • type: task (18)
  • priority: medium (15)
  • type: new feature (14)
  • priority: high (13)
  • required: FOR OFFICIAL RELEASE (13)
  • type: enhancement (5)
  • requestor: university (5)
  • requestor: RAL (4)
  • pillar: applications (4)
  • priority: low (1)

Top Pull Request Labels

  • pillar: community (2)
  • priority: medium (2)
  • requestor: Multiscale NWP Team (2)
  • type: task (2)
  • test (1)

Dependencies

.github/workflows/documentation.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • actions/upload-artifact v4 composite
docs/requirements.txt pypi
  • sphinx ==5.3.0
  • sphinx-design ==0.3.0
  • sphinx-gallery ==0.14.0
  • sphinx_rtd_theme ==1.3.0
  • sphinxcontrib-bibtex ==2.6.1

Score: 6.206575926724928