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

Dettmer, Jan; Dosso, S.E.; Holland, Charles W.


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...[Show more]

CollectionsANU Research Publications
Date published: 2013
Type: Conference paper
Source: Proceedings of Meetings on Acoustics
DOI: 10.1121/1.4799784


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