Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units

TL;DR

This paper demonstrates a GPU-accelerated Gaussian Beam Tracing method using CUDA, achieving up to 790x speedup over CPU implementations in complex acoustic simulations, with innovative solutions for handling irregular computations and memory constraints.

Contribution

The study introduces a GPU-optimized Gaussian Beam Tracing algorithm with novel handling of irregular loops and memory limitations, significantly improving performance over traditional CPU methods.

Findings

Performance up to 790 times faster in city environments

Performance up to 188 times faster in open environments

Established strategies for evaluating and reconstructing similar algorithms

Abstract

This study presents a reconstruction of the Gaussian Beam Tracing solution using CUDA, with a particular focus on the utilisation of GPU acceleration as a means of overcoming the performance limitations of traditional CPU algorithms in complex acoustic simulations. The algorithm is implemented and optimised on the NVIDIA RTX A6000 GPU, resulting in a notable enhancement in the performance of the Gaussian Beam Summation (GBS) process. In particular, the GPU-accelerated GBS algorithm demonstrated a significant enhancement in performance, reaching up to 790 times faster in city enviroment and 188 times faster in open plane enviroment compared to the original CPU-based program. To address the challenges of acceleration, the study introduce innovative solutions for handling irregular loops and GPU memory limitations, ensuring the efficient processing of large quantities of rays beyond the GPU's single-process capacity. Furthermore, this work established performance evaluation strategies crucial for analysing and reconstructing similar algorithms. Additionally, the study explored future directions for further accelerating the algorithm, laying the groundwork for ongoing improvements.