We present a parallel time-domain wave solver designed for large and high frequency acoustic domains. Our approach is based on a novel scalable method for dividing acoustic field computations specifically for large-scale distributed memory clusters using parallel Adaptive Rectangular Decomposition (ARD). In order to efficiently compute the acoustic field for large or high frequency domains, we need to take full advantage of the compute resources of large clusters. This is done with new algorithmic contributions, including a hypergraph partitioning scheme to reduce the communication cost between the cores on the cluster, a novel domain decomposition scheme that reduces the amount of numerical dispersion error introduced by the load balancing algorithm, and a revamped pipeline for parallel ARD computation that increases memory efficiency and reduces redundant computations. Our resulting parallel algorithm makes it possible to compute the sound pressure field for high frequencies in large environments that are thousands of cubic meters in volume. We highlight the performance of our system on large clusters with 16,000 cores on homogeneous indoor and outdoor benchmarks up to 10 kHz. To the best of our knowledge, this is the first time-domain parallel acoustic wave solver that can handle such large domains and frequencies.
Morales, N., Chavda, V., Mehra, R., & Manocha, D. (2017). MPARD: A high-frequency wave-based acoustic solver for very large compute clusters. Applied Acoustics, 121, 82–94. https://doi.org/10.1016/j.apacoust.2017.01.009