Multi GPU parallelization of maximum likelihood expectation maximization method for digital rock tomography data
نویسندگان
چکیده
Abstract Digital rock is an emerging area of physics, which involves scanning reservoir rocks using X-ray micro computed tomography (XCT) scanners and it for various petrophysical computations evaluations. The acquired CT projections are used to reconstruct the attenuation maps rock. image reconstruction problem can be solved by utilization analytical (such as Feldkamp–Davis–Kress (FDK) algorithm) or iterative methods. Analytical schemes typically computationally more efficient hence preferred large datasets such digital rocks. Iterative like maximum likelihood expectation maximization (MLEM) known generate accurate representation over scheme in limited data (and/or noisy) situations, however expensive. In this work, we have parallelized forward inverse operators MLEM algorithm on multiple graphics processing units (multi-GPU) platforms. multi-GPU implementation dividing volumes detector geometry into smaller modules (along with overlap regions). Each module was passed onto different GPU enable computation operations. We observed acceleration $$\sim 30$$ ? 30 times our approach compared multi-core CPU implementation. Further based obtained superior traditional FDK algorithm.
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2021
ISSN: ['2045-2322']
DOI: https://doi.org/10.1038/s41598-021-97833-z