  Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference
framework. The latest implementation of EDM, cppEDM, has only been used for
small datasets due to computational cost. With the growth of data collection
capabilities, there is a great need to identify causal relationships in large
datasets. We present mpEDM, a parallel distributed implementation of EDM
optimized for modern GPU-centric supercomputers. We improve the original
algorithm to reduce redundant computation and optimize the implementation to
fully utilize hardware resources such as GPUs and SIMD units. As a use case, we
run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an
entire animal brain sampled at single neuron resolution to identify dynamical
causation patterns across the brain. mpEDM is 1,530 X faster than cppEDM and a
dataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes.
This is the largest EDM causal inference achieved to date.

Distributed, Parallel, and Cluster Computing;Dynamical Systems;Quantitative Methods