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kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park, Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao
Nara Institute of Science and Technology, Nara, Japan
arXiv:2105.12301 [cs.DC], (26 May 2021)

@article{takahashi2021kedm,

   title={kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos},

   author={Takahashi, Keichi and Watanakeesuntorn, Wassapon and Ichikawa, Kohei and Park, Joseph and Takano, Ryousei and Haga, Jason and Sugihara, George and Pao, Gerald M.},

   year={2021}

}

Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series analysis framework. Despite its wide applicability, EDM was not scalable to large datasets due to its expensive computational cost. To overcome this obstacle, researchers have attempted and succeeded in accelerating EDM from both algorithmic and implementational aspects. In previous work, we developed a massively parallel implementation of EDM targeting HPC systems (mpEDM). However, mpEDM maintains different backends for different architectures. This design becomes a burden in the increasingly diversifying HPC systems, when porting to new hardware. In this paper, we design and develop a performance-portable implementation of EDM based on the Kokkos performance portability framework (kEDM), which runs on both CPUs and GPUs while based on a single codebase. Furthermore, we optimize individual kernels specifically for EDM computation, and use real-world datasets to demonstrate up to 5.5x speedup compared to mpEDM in convergent cross mapping computation.
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