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.

中文翻译：

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单神经元分辨率下全脑动力学的大规模并行因果推论

经验动态建模（EDM）是非线性时间序列因果推断框架。由于计算量大，EDM的最新实现cppEDM仅用于小型数据集。随着数据收集功能的增长，迫切需要识别大型数据集中的因果关系。我们介绍了mpEDM，这是针对现代GPU中心的超级计算机而优化的EDM并行分布式实现。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等硬件资源。作为一个用例，我们使用以单个神经元分辨率采样的整个动物大脑的数据集在AI桥接云基础设施（ABCI）上运行mpEDM，以识别整个大脑的动态因果关系模式。mpEDM为1 比cppEDM快530 X，并在199秒内在512个节点上分析了包含101,729个神经元的数据集。这是迄今为止最大的EDM因果推论。