A multi-language auto-differentiation module and its application to a parallel particle-in-cell code on distributed computers

TL;DR

This paper introduces a fast, transparent auto-differentiation module that can be easily integrated into simulation codes, demonstrated through its application to a parallel particle-in-cell code on distributed computers, enhancing sensitivity analysis and optimization.

Contribution

We develop a versatile auto-differentiation module that simplifies integration into simulation codes and demonstrate its effectiveness in a parallel particle-in-cell simulation on distributed systems.

Findings

Efficient auto-differentiation module integrated into PIC code

Enhanced sensitivity analysis capabilities on distributed systems

Potential for improved optimization workflows

Abstract

The auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to various input parameters. It provides an efficient method to study sensitivity of the simulation results with respect to the input parameters. Furthermore, it can be used in gradient based optimization methods for rapidly optimizing design parameters. In this paper, we present the development of a fast and transparent auto-differentiation module/class designed for easy integration into numerous simulation codes. As an application, this auto-differentiation module is integrated into a parallel particle-in-cell code with message passing interface (MPI) on distributed memory computers.