A GPU-accelerated Cartesian grid method is proposed for solving the heat, wave, and Schrodinger equations on irregular domains

TL;DR

This paper presents a GPU-accelerated Cartesian grid method for efficiently solving heat, wave, and Schrödinger equations on irregular domains with second-order accuracy and significant speedup over CPU implementations.

Contribution

It introduces a second-order, GPU-accelerated Cartesian grid approach using KFBI for solving elliptic PDEs related to heat, wave, and Schrödinger equations on irregular domains.

Findings

Achieves second-order accuracy for all three equations.

Provides a 30-fold speedup over CPU-based solvers.

Successfully handles irregular domains with high computational efficiency.

Abstract

This paper introduces a second-order method for solving general elliptic partial differential equations (PDEs) on irregular domains using GPU acceleration, based on Ying's kernel-free boundary integral (KFBI) method. The method addresses limitations imposed by CFL conditions in explicit schemes and accuracy issues in fully implicit schemes for the Laplacian operator. To overcome these challenges, the paper employs a series of second-order time discrete schemes and splits the Laplacian operator into explicit and implicit components. Specifically, the Crank-Nicolson method discretizes the heat equation in the temporal dimension, while the implicit scheme is used for the wave equation. The Schrodinger equation is treated using the Strang splitting method. By discretizing the temporal dimension implicitly, the heat, wave, and Schrodinger equations are transformed into a sequence of elliptic equations. The Laplacian operator on the right-hand side of the elliptic equation is obtained from the numerical scheme rather than being discretized and corrected by the five-point difference method. A Cartesian grid-based KFBI method is employed to solve the resulting elliptic equations. GPU acceleration, achieved through a parallel Cartesian grid solver, enhances the computational efficiency by exploiting high degrees of parallelism. Numerical results demonstrate that the proposed method achieves second-order accuracy for the heat, wave, and Schrodinger equations. Furthermore, the GPU-accelerated solvers for the three types of time-dependent equations exhibit a speedup of 30 times compared to CPU-based solvers.