论文标题
批处理的二阶伴随灵敏度减少了空间方法
Batched Second-Order Adjoint Sensitivity for Reduced Space Methods
论文作者
论文摘要
本文提出了一种从隐式非线性方程系统中提取二阶灵敏度的有效方法，这些方法是关于即将到来的图形处理单元（GPU）主导的计算机系统的。我们设计了一个自定义自动分化（AUTODIFF）后端，该后端通过在批处理中提取二阶信息来针对高度平行的体系结构。当非线性方程与减少的空间优化问题相关联时，我们利用批处理的伴随 - 附属算法中的平行反向模式积累来有效地计算问题的降低的Hessian。我们应用方法来提取与功率网络的平衡方程相关的简化Hessian，并在最大的情况下显示并行GPU实现的速度比基于UMFPACK的顺序CPU参考快30倍。
This paper presents an efficient method for extracting the second-order sensitivities from a system of implicit nonlinear equations on upcoming graphical processing units (GPU) dominated computer systems. We design a custom automatic differentiation (AutoDiff) backend that targets highly parallel architectures by extracting the second-order information in batch. When the nonlinear equations are associated to a reduced space optimization problem, we leverage the parallel reverse-mode accumulation in a batched adjoint-adjoint algorithm to compute efficiently the reduced Hessian of the problem. We apply the method to extract the reduced Hessian associated to the balance equations of a power network, and show on the largest instances that a parallel GPU implementation is 30 times faster than a sequential CPU reference based on UMFPACK.
