GPU-Based Implementation of Ptycho-ADMM for High Performance X-Ray Imaging

  • Pablo Enfedaque
  • Huibin Chang
  • Hari Krishnan
  • Stefano Marchesini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)

Abstract

X-ray imaging allows biologists to retrieve the atomic arrangement of proteins and doctors the capability to view broken bones in full detail. In this context, ptychography has risen as a reference imaging technique. It provides resolutions of one billionth of a meter, macroscopic field of view, or the capability to retrieve chemical or magnetic contrast, among other features. The goal is to reconstruct a 2D visualization of a sample from a collection of diffraction patterns generated from the interaction of a light source with the sample. The data collected is typically two orders of magnitude bigger than the final image reconstructed, so high performance solutions are normally desired. One of the latest advances in ptychography imaging is the development of Ptycho-ADMM, a new ptychography reconstruction algorithm based on the Alternating Direction Method of Multipliers (ADMM). Ptycho-ADMM provides faster convergence speed and better quality reconstructions, all while being more resilient to noise in comparison with state-of-the-art methods. The downside of Ptycho-ADMM is that it requires additional computation and a larger memory footprint compared to simpler solutions. In this paper we tackle the computational requirements of Ptycho-ADMM, and design the first high performance multi-GPU solution of the method. We analyze and exploit the parallelism of Ptycho-ADMM to make use of multiple GPU devices. The proposed implementation achieves reconstruction times comparable to other GPU-accelerated high performance solutions, while providing the enhanced reconstruction quality of the Ptycho-ADMM method.

Notes

Acknowledgment

This work was partially funded by the Center for Applied Mathematics for Energy Research Applications, a joint ASCR- BES funded project within the Office of Science, US Department of Energy, under contract number DOE-DE-AC03-76SF00098, and by the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231.

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • Pablo Enfedaque
    • 1
  • Huibin Chang
    • 1
    • 2
  • Hari Krishnan
    • 1
  • Stefano Marchesini
    • 1
  1. 1.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.School of Mathematical SciencesTianjin Normal UniversityTianjinChina

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