Massively parallel causal inference of whole brain dynamics at single neuron resolution

Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa, Joseph Park, George Sugihara, Ryousei Takano, Jason Haga, Gerald M. Pao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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× 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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 26th International Conference on Parallel and Distributed Systems, ICPADS 2020
PublisherIEEE Computer Society
Pages196-205
Number of pages10
ISBN (Electronic)9781728190747
DOIs
Publication statusPublished - 2020 Dec
Event26th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2020 - Virtual, Hong Kong, Hong Kong
Duration: 2020 Dec 22020 Dec 4

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2020-December
ISSN (Print)1521-9097

Conference

Conference26th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2020
Country/TerritoryHong Kong
CityVirtual, Hong Kong
Period20/12/220/12/4

Keywords

  • Causal Inference
  • Empirical Dynamic Modeling
  • GPU
  • High-Performance Computing
  • Neuroscience
  • Parallel Distributed Computing

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