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

Jaya Prakash1, Umang Agarwal2, Phaneendra K Yalavarthy3

  • 1Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru, 560 012, India. jayap@iisc.ac.in.

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|September 18, 2021
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This summary is machine-generated.

This study accelerates digital rock analysis using multi-GPU parallelization for the Maximum Likelihood Expectation Maximization (MLEM) algorithm. This approach significantly enhances image reconstruction accuracy and computational speed compared to traditional methods.

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Area of Science:

  • Digital rock physics
  • Reservoir characterization
  • X-ray micro computed tomography (XCT)

Background:

  • Digital rock analysis utilizes XCT scanning for petrophysical evaluations.
  • Image reconstruction from XCT data can employ analytical (e.g., FDK) or iterative (e.g., MLEM) methods.
  • Iterative methods like MLEM offer higher accuracy but are computationally intensive, limiting their application to large datasets.

Purpose of the Study:

  • To accelerate the computationally expensive MLEM algorithm for digital rock image reconstruction.
  • To implement and evaluate a multi-GPU parallelized MLEM approach.
  • To compare the performance and accuracy of the multi-GPU MLEM against traditional FDK algorithms and CPU-based MLEM.

Main Methods:

  • Parallelization of MLEM forward and inverse operators across multiple GPUs.
  • Implementation strategy involving modular division of rock volumes and detector geometry with overlap regions.
  • Performance comparison with multi-core CPU implementations and the FDK algorithm.

Main Results:

  • Achieved a significant acceleration factor of [Formula: see text] using the multi-GPU MLEM approach compared to multi-core CPU implementations.
  • Demonstrated superior image reconstruction quality with the multi-GPU MLEM compared to the conventional FDK algorithm.
  • Validated the efficiency and accuracy benefits of parallelized iterative reconstruction for digital rock analysis.

Conclusions:

  • Multi-GPU parallelization effectively addresses the computational challenges of iterative image reconstruction in digital rock physics.
  • The developed multi-GPU MLEM method provides a faster and more accurate alternative to traditional FDK algorithms for analyzing large digital rock datasets.
  • This advancement has the potential to improve reservoir characterization and petrophysical evaluations through enhanced digital rock analysis.