https://github.com/keichi/kedm
A high-performance implementation of Empirical Dynamic Modeling (EDM)
Science Score: 39.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 15 DOI reference(s) in README -
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.8%) to scientific vocabulary
Keywords
Repository
A high-performance implementation of Empirical Dynamic Modeling (EDM)
Basic Info
- Host: GitHub
- Owner: keichi
- License: mit
- Language: C++
- Default Branch: master
- Homepage: https://kedm.readthedocs.io/
- Size: 586 KB
Statistics
- Stars: 17
- Watchers: 3
- Forks: 5
- Open Issues: 5
- Releases: 0
Topics
Metadata Files
README.md
kEDM
kEDM (Kokkos-EDM) is a high-performance implementation of the Empirical Dynamical Modeling (EDM) framework. The goal of kEDM is to provide an optimized and parallelized implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring compatibility with the original reference implementation (cppEDM).
Following EDM algorithms are currently implemented in kEDM:
- Simplex projection [1]
- Sequential Locally Weighted Global Linear Maps (S-Map) [2]
- Convergent Cross Mapping (CCM) [3]
Installation
CPU (Linux and macOS)
pip3 install kedm
NVIDIA GPU (CUDA 11.2 or later)
pip3 install kedm-11x
NVIDIA GPU (CUDA 12.0 or later)
pip3 install kedm-12x
Citing
Please cite the following papers if you find kEDM useful:
- Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183, Jun. 2023. 10.1109/ACCESS.2023.3289836
- Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park, Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A Performance-portable Implementation of Empirical Dynamical Modeling," Practice & Experience in Advanced Research Computing (PEARC 2021), Jul. 2021. 10.1145/3437359.3465571
References
- George Sugihara, Robert May, "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series," Nature, vol. 344, pp. 734–741, 1990. 10.1038/344734a0
- George Sugihara, "Nonlinear forecasting for the classification of natural time series. Philosophical Transactions," Physical Sciences and Engineering, vol. 348, no. 1688, pp. 477–495, 1994. 10.1098/rsta.1994.0106
- George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems," Science, vol. 338, pp. 496–500, 2012. 10.1126/science.1227079
Owner
- Name: Keichi Takahashi
- Login: keichi
- Kind: user
- Location: Sendai, Japan
- Company: Tohoku University
- Website: https://keichi.dev
- Twitter: _keichi_
- Repositories: 120
- Profile: https://github.com/keichi
I'm a computer scientist working on high-performance computing.
GitHub Events
Total
- Issues event: 1
- Watch event: 3
- Delete event: 7
- Issue comment event: 2
- Push event: 44
- Pull request event: 12
- Create event: 8
Last Year
- Issues event: 1
- Watch event: 3
- Delete event: 7
- Issue comment event: 2
- Push event: 44
- Pull request event: 12
- Create event: 8
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Keichi Takahashi | k****t@m****m | 308 |
| SoftwareLiteracy | J****k@S****g | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 16
- Total pull requests: 42
- Average time to close issues: 5 months
- Average time to close pull requests: 9 days
- Total issue authors: 4
- Total pull request authors: 3
- Average comments per issue: 1.75
- Average comments per pull request: 0.29
- Merged pull requests: 37
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 8
- Average time to close issues: N/A
- Average time to close pull requests: about 2 months
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.13
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- keichi (9)
- nsankar (2)
- davidgwyrick (2)
- SoftwareLiteracy (1)
Pull Request Authors
- keichi (40)
- SoftwareLiteracy (2)
- dependabot[bot] (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
-
Total downloads:
- pypi 1,949 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 29
- Total maintainers: 1
pypi.org: kedm
A high-performance implementation of the Empirical Dynamic Modeling (EDM) framework
- Homepage: https://github.com/keichi/kEDM
- Documentation: https://kedm.readthedocs.io/
- License: mit
-
Latest release: 0.9.2
published 8 months ago
Rankings
Maintainers (1)
pypi.org: kedm-cuda12x
A high-performance implementation of the Empirical Dynamic Modeling (EDM) framework
- Homepage: https://github.com/keichi/kEDM
- Documentation: https://kedm.readthedocs.io/
- License: mit
-
Latest release: 0.9.2
published 8 months ago
Rankings
Maintainers (1)
pypi.org: kedm-cuda11x
A high-performance implementation of the Empirical Dynamic Modeling (EDM) framework
- Homepage: https://github.com/keichi/kEDM
- Documentation: https://kedm.readthedocs.io/
- License: mit
-
Latest release: 0.9.2
published 8 months ago
Rankings
Maintainers (1)
Dependencies
- Jinja2 <3.1
- Sphinx ==3.5.4
- kedm *
- sphinx-rtd-theme ==0.5.2
- numpy >=1.7.0
- actions/checkout v3 composite
- actions/download-artifact v3 composite
- actions/upload-artifact v3 composite
- joerick/cibuildwheel v2.12.0 composite
- pypa/gh-action-pypi-publish release/v1 composite