Scalable Empirical Dynamic Modeling with Parallel Computing and Approximate k-NN Search
Empirical Dynamic Modeling (EDM) is a mathematical framework for modeling and predicting non-linear time series data. Although EDM is increasingly adopted in various research fields, its application to large-scale data has been limited due to its high computational cost. This article presents kEDM,...
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| Veröffentlicht in: | IEEE access 2023-01, Vol.11, p.1-1 |
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| creator | Takahashi, Keichi Ichikawa, Kohei Park, Joseph Pao, Gerald M. |
| description | Empirical Dynamic Modeling (EDM) is a mathematical framework for modeling and predicting non-linear time series data. Although EDM is increasingly adopted in various research fields, its application to large-scale data has been limited due to its high computational cost. This article presents kEDM, a high-performance implementation of EDM for analyzing large-scale time series datasets. kEDM adopts the Kokkos performance-portable programming model to efficiently run on both CPU and GPU while sharing a single code base. We also conduct hardware-specific optimization of performance-critical kernels. kEDM achieved up to 6.58× speedup in pairwise causal inference of real-world biology datasets compared to an existing EDM implementation. Furthermore, we integrate multiple approximate k-NN search algorithms into EDM to enable the analysis of extremely large datasets that were intractable with conventional EDM based on exhaustive k-NN search. EDM-based time series forecast enhanced with approximate k-NN search demonstrated up to 790× speedup compared to conventional Simplex projection with less than 1% increase in MAPE. |
| doi_str_mv | 10.1109/ACCESS.2023.3289836 |
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| subjects | Computational modeling Cost analysis Data analysis Datasets Dynamic models Empirical analysis Empirical Dynamic Modeling Graphics processing units Hardware High performance computing High-Performance Data Analytics Instruction sets Optimization Parallel processing Performance Portability Programming Search algorithms Time series Time series analysis |
| title | Scalable Empirical Dynamic Modeling with Parallel Computing and Approximate k-NN Search |
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