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Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inferenceframework. The latest implementation of EDM, cppEDM, has only been used forsmall datasets due to computational cost. With the growth of data collectioncapabilities, there is a great need to identify causal relationships in largedatasets. We present mpEDM, a parallel distributed implementation of EDMoptimized for modern GPU-centric supercomputers. We improve the originalalgorithm to reduce redundant computation and optimize the implementation tofully utilize hardware resources such as GPUs and SIMD units. As a use case, werun mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of anentire animal brain sampled at single neuron resolution to identify dynamicalcausation patterns across the brain. mpEDM is 1,530 X faster than cppEDM and adataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes.This is the largest EDM causal inference achieved to date.

Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution