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Multi GPU parallelization of maximum likelihood expectation maximization method for digital rock tomography data

Prakash, J and Agarwal, U and Yalavarthy, PK (2021) Multi GPU parallelization of maximum likelihood expectation maximization method for digital rock tomography data. In: Scientific Reports, 11 (1).

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Official URL: https://doi.org/10.1038/s41598-021-97833-z


Digital rock is an emerging area of rock physics, which involves scanning reservoir rocks using X-ray micro computed tomography (XCT) scanners and using it for various petrophysical computations and evaluations. The acquired micro CT projections are used to reconstruct the X-ray attenuation maps of the rock. The image reconstruction problem can be solved by utilization of analytical (such as Feldkamp�Davis�Kress (FDK) algorithm) or iterative methods. Analytical schemes are typically computationally more efficient and hence preferred for large datasets such as digital rocks. Iterative schemes like maximum likelihood expectation maximization (MLEM) are known to generate accurate image representation over analytical scheme in limited data (and/or noisy) situations, however iterative schemes are computationally expensive. In this work, we have parallelized the forward and inverse operators used in the MLEM algorithm on multiple graphics processing units (multi-GPU) platforms. The multi-GPU implementation involves dividing the rock volumes and detector geometry into smaller modules (along with overlap regions). Each of the module was passed onto different GPU to enable computation of forward and inverse operations. We observed an acceleration of � 30 times using our multi-GPU approach compared to the multi-core CPU implementation. Further multi-GPU based MLEM obtained superior reconstruction compared to traditional FDK algorithm. © 2021, The Author(s).

Item Type: Journal Article
Publication: Scientific Reports
Publisher: Nature Research
Additional Information: The copyright for this article belongs to Authors
Keywords: acceleration; article; controlled study; geometry; human; maximum likelihood expectation maximization; tomography
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Date Deposited: 03 Dec 2021 08:34
Last Modified: 03 Dec 2021 08:34
URI: http://eprints.iisc.ac.in/id/eprint/70235

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