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

Wassapon Watanakeesuntorn, Keichi Takahashi, Koheix Ichikawa, Joseph Park, G. Sugihara, Ryousei Takano, J. Haga, G. Pao
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引用次数: 5

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.
单神经元分辨率下全脑动力学的大规模并行因果推理
经验动态模型(EDM)是一种非线性时间序列因果推理框架。由于计算成本的原因,EDM的最新实现cppEDM仅用于小数据集。随着数据收集能力的增长,识别大型数据集中的因果关系非常有必要。我们提出了mpEDM,一种针对现代gpu中心超级计算机优化的并行分布式EDM实现。我们改进了原始算法以减少冗余计算,并优化了实现以充分利用gpu和SIMD单元等硬件资源。作为一个用例,我们在AI桥接云基础设施(ABCI)上运行mpEDM,使用以单个神经元分辨率采样的整个动物大脑的数据集来识别整个大脑的动态因果模式。mpEDM比cppEDM快1530倍,在199秒内分析了包含101729个神经元的512个节点的数据集。这是迄今为止实现的最大的EDM因果推理。
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