High Performance Partial Coherent X-Ray Ptychography

  • Pablo EnfedaqueEmail author
  • Huibin Chang
  • Bjoern Enders
  • David Shapiro
  • Stefano Marchesini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11536)


During the last century, X-ray science has enabled breakthrough discoveries in fields as diverse as medicine, material science or electronics, and recently, ptychography has risen as a reference imaging technique in the field. It provides resolutions of a billionth of a meter, macroscopic field of view, or the capability to retrieve chemical or magnetic contrast, among other features. The goal of ptychography 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. Reconstruction involves solving a nonlinear optimization problem employing a large amount of measured data—typically two orders of magnitude bigger than the reconstructed sample—so high performance solutions are normally required. A common problem in ptychography is that the majority of the flux from the light sources is often discarded to define the coherence of an illumination. Gradient Decomposition of the Probe (GDP) is a novel method devised to address this issue. It provides the capability to significantly improve the quality of the image when partial coherence effects take place, at the expense of a three-fold increase of the memory requirements and computation. This downside, along with the fine-grained degree of parallelism of the operations involved in GDP, makes it an ideal target for GPU acceleration. In this paper we propose the first high performance implementation of GDP for partial coherence X-ray ptychography. The proposed solution exploits an efficient data layout and multi-gpu parallelism to achieve massive acceleration and efficient scaling. The experimental results demonstrate the enhanced reconstruction quality and performance of our solution, able process up to 4 million input samples per second on a single high-end workstation, and compare its performance with a reference HPC ptychography pipeline.



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, and the Advanced Light Source under contract number DOE-DE-AC03-76SF00098. This work was also partially supported by the National Natural Science Foundation of China (11871372).


  1. 1.
    Rodenburg, J.M.: Ptychography and related diffractive imaging methods. Adv. Imaging Electron Phys. 150, 87–184 (2008)CrossRefGoogle Scholar
  2. 2.
    Shi, X., et al.: Soft x-ray ptychography studies of nanoscale magnetic and structural correlations in thin SmCo\(_5\) films. Appl. Phys. Lett. 108(9), 094103 (2016)CrossRefGoogle Scholar
  3. 3.
    Giewekemeyer, K., et al.: Quantitative biological imaging by ptychographic x-ray diffraction microscopy. Proc. Nat. Acad. Sci. 107(2), 529–534 (2010)CrossRefGoogle Scholar
  4. 4.
    Shapiro, D.A., et al.: Chemical composition mapping with nanometre resolution by soft x-ray microscopy. Nat. Photonics 8(10), 765–769 (2014)CrossRefGoogle Scholar
  5. 5.
    Holler, M., et al.: High-resolution non-destructive three-dimensional imaging of integrated circuits. Nature 543(7645), 402–406 (2017)CrossRefGoogle Scholar
  6. 6.
    Chang, H., Enfedaque, P., Lou, Y., Marchesini, S.: Partially coherent ptychography by gradient decomposition of the probe. Acta Crystallogr. Sect. A: Found. Adv. 74(3), 157–169 (2018)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Marchesini, S., et al.: SHARP: a distributed, GPU-based ptychographic solver. J. Appl. Crystallogr. 49(4), 1245–1252 (2016)CrossRefGoogle Scholar
  8. 8.
    Nashed, Y.S., Vine, D.J., Peterka, T., Deng, J., Ross, R., Jacobsen, C.: Parallel ptychographic reconstruction. Opt. Express 22(26), 32 082–32 097 (2014)CrossRefGoogle Scholar
  9. 9.
    Enfedaque, P., Chang, H., Krishnan, H., Marchesini, S.: GPU-based implementation of ptycho-ADMM for high performance X-ray imaging. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10860, pp. 540–553. Springer, Cham (2018). Scholar
  10. 10.
    Thibault, P., Dierolf, M., Bunk, O., Menzel, A., Pfeiffer, F.: Probe retrieval in ptychographic coherent diffractive imaging. Ultramicroscopy 109(4), 338–343 (2009)CrossRefGoogle Scholar
  11. 11.
    Maiden, A.M., Rodenburg, J.M.: An improved ptychographical phase retrieval algorithm for diffractive imaging. Ultramicroscopy 109(10), 1256–1262 (2009)CrossRefGoogle Scholar
  12. 12.
    Elser, V.: Phase retrieval by iterated projections. J. Opt. Soc. Am. A 20(1), 40–55 (2003)CrossRefGoogle Scholar
  13. 13.
    Thibault, P., Guizar-Sicairos, M.: Maximum-likelihood refinement for coherent diffractive imaging. New J. Phys. 14(6), 063004 (2012)CrossRefGoogle Scholar
  14. 14.
    Hesse, R., Luke, D.R., Sabach, S., Tam, M.K.: Proximal heterogeneous block implicit-explicit method and application to blind ptychographic diffraction imaging. SIAM J. Imaging Sci. 8(1), 426–457 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Luke, D.R.: Relaxed averaged alternating reflections for diffraction imaging. Inverse Prob. 21(1), 37–50 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Glowinski, R., Le Tallec, P.: Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics. SIAM, Philadelphia (1989)CrossRefGoogle Scholar
  17. 17.
    Wu, C., Tai, X.-C.: Augmented Lagrangian method, dual methods and split-Bregman iterations for ROF, vectorial TV and higher order models. SIAM J. Imaging Sci. 3(3), 300–339 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Chang, H., Enfedaque, P., Marchesini, S.: Blind ptychographic phase retrieval via convergent alternating direction method of multipliers. SIAM J. Imaging Sci. 12(1), 153–185 (2019). Scholar
  19. 19.
    Chang, H., et al.: Advanced denoising for x-ray ptychography. Opt. Express 27(8), 10395–10418 (2019). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pablo Enfedaque
    • 1
    Email author
  • Huibin Chang
    • 1
    • 2
  • Bjoern Enders
    • 3
  • David Shapiro
    • 4
  • Stefano Marchesini
    • 1
  1. 1.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.School of Mathematical SciencesTianjin Normal UniversityTianjinChina
  3. 3.National Energy Research Scientific Computing CenterLawrence Berkeley National LaboratoryBerkeleyUSA
  4. 4.Advanced Light SourceLawrence Berkeley National LaboratoryBerkeleyUSA

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