Domain decomposition of the modified Born series approach for large-scale wave propagation simulations

TL;DR

This paper introduces a domain decomposition technique for the modified Born series that enables large-scale wave simulations across multiple GPUs, significantly increasing the problem size and maintaining accuracy and convergence.

Contribution

It presents a scalable domain decomposition method for the MBS, allowing parallel computation over multiple GPUs without sacrificing accuracy or convergence.

Findings

Simulated a 3.28×10^7 wavelength problem in 45 minutes

Achieved scalable wave propagation simulation on multiple GPUs

Maintained accuracy and convergence with the new method

Abstract

The modified Born series (MBS) is a fast and accurate method for simulating wave propagation in complex structures. In the current implementation of the MBS, the simulation size is limited by the working memory of a single computer or graphics processing unit (GPU). Here, we present a domain decomposition method that enhances the scalability of the MBS by distributing the computations over multiple GPUs, while maintaining its accuracy, memory efficiency, and guaranteed monotonic convergence. With this new method, the computations can be performed in parallel, and a larger simulation size is possible as it is no longer limited to the memory size of a single computer or GPU. We show how to decompose large problems over subdomains and demonstrate our approach by solving the Helmholtz problem for a complex structure of $3.28\cdot 10^7$ cubic wavelengths ($320 \times 320 \times 320$ wavelengths) in just $45$ minutes with a dual-GPU simulation.