### Abstract

Bayesian inference algorithms in geoacoustic inversion have high computational requirements on multiple computational scales. Predicting (modeling) data to match observations represents fine-grained computations which often cannot be implemented efficiently on CPU clusters since high latency and communication overhead outweigh parallelization gains. However, GPUs, which operate efficiently on 100,000s of parallel treads with low latency and high bandwidth, can provide signficant performance gains. Bayesian sampling schemes are generally coarse-grained, and can be implemented efficiently in parallel on multi-core/cluster architectures. For example, population Monte Carlo methods simulate many Markov chains in parallel, with chains running independently between interactions (at predefined intervals) which exchange information throughout the population, substantially increasing sampling efficiency. This paper combines fine- and coarse-grained parallelization to profoundly improve the efficiency of geoacoustic inversion of seabed reflection data. Spherical-wave reflection-coefficient predictions, which require solving the Sommerfeld integral for a large number of grazing angles and frequencies, constitute fine-grained, data-parallel computations which are implemented efficiently on a GPU. Sampling is based on population Monte Carlo simulation with chain interactions as exchange and crossover moves. The algorithm is applied to data from the Malta Plateau to study the frequency dependence of sound velocity and attenuation in marine sediments.

Original language | English (US) |
---|---|

Article number | 070094 |

Journal | Proceedings of Meetings on Acoustics |

Volume | 19 |

DOIs | |

State | Published - Jun 19 2013 |

Event | 21st International Congress on Acoustics, ICA 2013 - 165th Meeting of the Acoustical Society of America - Montreal, QC, Canada Duration: Jun 2 2013 → Jun 7 2013 |

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### All Science Journal Classification (ASJC) codes

- Acoustics and Ultrasonics

### Cite this

*Proceedings of Meetings on Acoustics*,

*19*, [070094]. https://doi.org/10.1121/1.4799784

}

*Proceedings of Meetings on Acoustics*, vol. 19, 070094. https://doi.org/10.1121/1.4799784

**Exploiting data parallelism and population Monte Carlo on massively-parallel architectures for geoacoustic inversion.** / Dettmer, Jan; Dosso, Stan E.; Holland, Charles.

Research output: Contribution to journal › Conference article

TY - JOUR

T1 - Exploiting data parallelism and population Monte Carlo on massively-parallel architectures for geoacoustic inversion

AU - Dettmer, Jan

AU - Dosso, Stan E.

AU - Holland, Charles

PY - 2013/6/19

Y1 - 2013/6/19

N2 - Bayesian inference algorithms in geoacoustic inversion have high computational requirements on multiple computational scales. Predicting (modeling) data to match observations represents fine-grained computations which often cannot be implemented efficiently on CPU clusters since high latency and communication overhead outweigh parallelization gains. However, GPUs, which operate efficiently on 100,000s of parallel treads with low latency and high bandwidth, can provide signficant performance gains. Bayesian sampling schemes are generally coarse-grained, and can be implemented efficiently in parallel on multi-core/cluster architectures. For example, population Monte Carlo methods simulate many Markov chains in parallel, with chains running independently between interactions (at predefined intervals) which exchange information throughout the population, substantially increasing sampling efficiency. This paper combines fine- and coarse-grained parallelization to profoundly improve the efficiency of geoacoustic inversion of seabed reflection data. Spherical-wave reflection-coefficient predictions, which require solving the Sommerfeld integral for a large number of grazing angles and frequencies, constitute fine-grained, data-parallel computations which are implemented efficiently on a GPU. Sampling is based on population Monte Carlo simulation with chain interactions as exchange and crossover moves. The algorithm is applied to data from the Malta Plateau to study the frequency dependence of sound velocity and attenuation in marine sediments.

AB - Bayesian inference algorithms in geoacoustic inversion have high computational requirements on multiple computational scales. Predicting (modeling) data to match observations represents fine-grained computations which often cannot be implemented efficiently on CPU clusters since high latency and communication overhead outweigh parallelization gains. However, GPUs, which operate efficiently on 100,000s of parallel treads with low latency and high bandwidth, can provide signficant performance gains. Bayesian sampling schemes are generally coarse-grained, and can be implemented efficiently in parallel on multi-core/cluster architectures. For example, population Monte Carlo methods simulate many Markov chains in parallel, with chains running independently between interactions (at predefined intervals) which exchange information throughout the population, substantially increasing sampling efficiency. This paper combines fine- and coarse-grained parallelization to profoundly improve the efficiency of geoacoustic inversion of seabed reflection data. Spherical-wave reflection-coefficient predictions, which require solving the Sommerfeld integral for a large number of grazing angles and frequencies, constitute fine-grained, data-parallel computations which are implemented efficiently on a GPU. Sampling is based on population Monte Carlo simulation with chain interactions as exchange and crossover moves. The algorithm is applied to data from the Malta Plateau to study the frequency dependence of sound velocity and attenuation in marine sediments.

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UR - http://www.scopus.com/inward/citedby.url?scp=84878971659&partnerID=8YFLogxK

U2 - 10.1121/1.4799784

DO - 10.1121/1.4799784

M3 - Conference article

VL - 19

JO - Proceedings of Meetings on Acoustics

JF - Proceedings of Meetings on Acoustics

SN - 1939-800X

M1 - 070094

ER -