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 $5.5\times$ speedup compared to mpEDM in convergent cross mapping computation.

中文翻译：

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kEDM：使用Kokkos的经验动态建模的性能便携式实现

经验动态建模（EDM）是最新的非线性时间序列分析框架。尽管EDM具有广泛的适用性，但由于其昂贵的计算成本，因此无法扩展到大型数据集。为了克服这一障碍，研究人员已经尝试并成功地从算法和实现方面加速了EDM。在以前的工作中，我们开发了针对HPC系统（mpEDM）的EDM大规模并行实现。但是，mpEDM为不同的体系结构维护不同的后端。当移植到新硬件时，这种设计已成为日益多样化的HPC系统的负担。在本文中，我们基于Kokkos性能可移植性框架（kEDM）设计和开发了性能可移植的EDM实现，该框架可在单个代码库的基础上在CPU和GPU上运行。此外，