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).
|
PDF
sci_rep_11-01_2021.pdf - Published Version Download (5MB) | Preview |
|
|
PDF
41598_2021_97833_MOESM1_ESM.pdf - Published Supplemental Material Download (482kB) | Preview |
Abstract
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 |
Actions (login required)
View Item |