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

TL;DR

This paper introduces mpEDM, a GPU-optimized, parallel implementation of Empirical Dynamic Modeling that significantly accelerates causal inference in large-scale neural datasets, enabling analysis of entire brain dynamics at single neuron resolution.

Contribution

The paper presents mpEDM, a novel, highly optimized parallel implementation of EDM for large datasets, achieving unprecedented speed and scale in causal inference of brain activity.

Findings

mpEDM is 1,530 times faster than previous cppEDM.

Analyzed a dataset of 101,729 neurons in 199 seconds.

Largest EDM causal inference to date.

Abstract

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.