A parallel time-domain wave simulator based on rectangular decomposition for distributed memory architectures

Morales, Nicolás; Mehra, Ravish; Manocha, Dinesh

doi:10.1016/j.apacoust.2015.03.017

Cited by 16 publications

(19 citation statements)

References 21 publications

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“…Because the evaluation cost of the local update of a subdomain is linearly related to the volume of that subdomain, the discrepancy in subdomain sizes can cause load imbalance problems where all the cores wait for one core to finish computing the local update. In fact, without modifying subdomain sizes, increasing the number of cores does not provide any speedup at all [26].…”

Section: Load Balancingmentioning

confidence: 99%

“…In this section, we provide a brief overview of the ARD method and a description of the parallel ARD pipeline. More details about these methods are available in [29,25,26]. ARD is a domain decomposition approach.…”

Section: Ard Backgroundmentioning

confidence: 99%

“…The parallel ARD method is an adaptation of the ARD method for distributed compute clusters and shared memory machines [26]. MPARD is based on the same framework as parallel ARD, so this section will go over relevant implementation details.…”

Section: Parallel Ardmentioning

confidence: 99%

“…ARD is a domain decomposition method that subdivides the computational domain into rectangular regions. One of the main advantages of ARD is the greatly reduced computational and memory requirements over more traditional methods like FDTD [26].…”

Section: Introductionmentioning

confidence: 99%

“…In order to deal with these requirements, a parallel distributed version of ARD was developed [26]. However, this approach was limited to smaller scenes and compute clusters and still had high computational requirements.…”

Section: Introductionmentioning

confidence: 99%

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MPARD: A high-frequency wave-based acoustic solver for very large compute clusters

Morales¹,

Chavda²,

Mehra³

et al. 2017

Applied Acoustics

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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 16000 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.

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Section: Load Balancingmentioning

confidence: 99%

Section: Ard Backgroundmentioning

confidence: 99%

Section: Parallel Ardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MPARD: A high-frequency wave-based acoustic solver for very large compute clusters

Morales¹,

Chavda²,

Mehra³

et al. 2017

Applied Acoustics

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Receiver placement for speech enhancement using sound propagation optimization

2019

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A common problem in acoustic design is the placement of speakers or receivers for public address systems, telecommunications, and home smart speakers or digital personal assistants. We present a novel algorithm to automatically place a speaker or receiver in a room to improve the intelligibility of spoken phrases in a design. Our technique uses a sound propagation optimization formulation to maximize the Speech Transmission Index (STI) by computing an optimal location of the sound receiver. We use an efficient and accurate hybrid sound propagation technique on complex 3D models to compute the Room Impulse Responses (RIR) and evaluate their impact on the STI. The overall algorithm computes a globally optimal position of the receiver that reduces the effects of reverberation and noise over many source positions. We evaluate our algorithm on various indoor 3D models, all showing significant improvement in STI, based on accurate sound propagation.

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Low resolution fourier synthesis modelling for underwater acoustic channel impulse response

Qiu

Wang

et al. 2022

Applied Acoustics

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A parallel time-domain wave simulator based on rectangular decomposition for distributed memory architectures

Cited by 16 publications

References 21 publications

MPARD: A high-frequency wave-based acoustic solver for very large compute clusters

MPARD: A high-frequency wave-based acoustic solver for very large compute clusters

Receiver placement for speech enhancement using sound propagation optimization

Low resolution fourier synthesis modelling for underwater acoustic channel impulse response

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