论文标题
单个神经元分辨率的全脑动力学的大量平行因果推断
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
论文作者
论文摘要
经验动态建模（EDM）是一个非线性时间序列因果推理框架。 EDM的最新实现CPPEDM仅由于计算成本而仅用于小型数据集。随着数据收集功能的增长，非常需要在大型数据集中识别因果关系。我们提出MPEDM，这是针对现代GPU中心超级计算机优化的EDM的并行分布式实现。我们改进了原始算法以减少冗余计算并优化实现，以充分利用GPU和SIMD单元等硬件资源。作为用例，我们使用以单个神经元分辨率采样的整个动物脑的数据集在AI桥接云基础架构（ABCI）上运行MPEDM，以识别大脑中的动态因果关系模式。 MPEDM比CPPEDM快1,530 x，在512个节点的199秒内分析了包含101,729个神经元的数据集。这是迄今为止最大的EDM因果推论。
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.
