Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
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DOI：
10.1109/icpads51040.2020.00035
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Wassapon Watanakeesuntorn;Keichi Takahashi;Koheix Ichikawa;Joseph Park;G. Sugihara;Ryousei Takano;J. Haga;G. Pao
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文献类型：
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作者：
Wassapon Watanakeesuntorn;Keichi Takahashi;Koheix Ichikawa;Joseph Park;G. Sugihara;Ryousei Takano;J. Haga;G. Pao
经验动态建模（EDM）是一个非线性时间序列推理框架。大型数据集中的因果关系。计算并优化实现，以充分利用硬件资源（例如GPU和SIMD单元）作为用例。在整个大脑中，MPEDM比CPPEDM快1,530×，并且在512个节点上分析了含有101,729个神经元的数据集是迄今为止最大的EDM因果推论。
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.