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kEDM

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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 HPC hardware (Intel Xeon, AMD EPYC, NVIDIA GPUs, Fujitsu A64FX, etc.) while ensuring compatibility with the 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]

Citing

Please cite the following paper if you find kEDM useful:

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.

References

  1. 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
  2. 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
  3. 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

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A high-performance implementation of Empirical Dynamic Modeling (EDM) using Kokkos

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