Fast computation of scattering maps of nanostructures using graphical processing units

Favre‐Nicolin, V.; Coraux, Johann; Richard, Marie-Ingrid; Renevier, H.

doi:10.1107/s0021889811009009

Cited by 38 publications

(32 citation statements)

References 36 publications

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“…See Section 4. (5) scattering: legacy GPU-accelerated kinematical scattering calculations, as described by Favre-Nicolin et al (2011).…”

Section: Pynx Toolkit Organizationmentioning

confidence: 99%

“…In this article we will present the open-source coherent X-ray imaging modules of the PyNX toolkit. In the previous versions (Favre-Nicolin et al, 2011;Mandula et al, 2016), GPU-accelerated computing was only available for scattering calculations (which are unchanged), whereas this new version is a complete rewrite of the ptychography module and adds tools for CDI and wavefront calculations, all GPU accelerated. We will first present an outline of the toolkit organization, followed by details of the operator-based approach which is used to simplify the development of custom algorithms, and finally the available command-line scripts.…”

Section: Introductionmentioning

confidence: 99%

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PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators

et al. 2020

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The open-source PyNX toolkit has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPUs) to achieve high-performance computing speeds. The toolkit can be used for coherent diffraction imaging (CDI), ptychography and wavefront propagation, in the far- or near-field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles…) can be implemented in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large ptychography data sets. Command-line scripts are available for on-line CDI and ptychography analysis, either from raw beamline data sets or using the coherent X-ray imaging data format.

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“…See Section 4. (5) scattering: legacy GPU-accelerated kinematical scattering calculations, as described by Favre-Nicolin et al (2011).…”

Section: Pynx Toolkit Organizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators

et al. 2020

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“…Besides possible artifacts induced by the Gaussian assumption, FFT in reciprocal space is limited to a triperiodic grid, inappropriate for simulations of curved surfaces (Ewald spheres) (Favre-Nicolin et al, 2011). Currently, direct diffraction simulations for systems consisting of millions of atoms are performed by the central processing unit (CPU) MPI (Message Passing Interface) parallel diffraction package (Coleman et al, 2013(Coleman et al, , 2014 integrated in the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) (Plimpton, 1995), or with the stand-alone CPU MPI parallel code SLADS (Chen et al, 2017) and single-node graphics processing unit (GPU) CUDA (Compute Unified Device Architecture) parallel computations (Favre-Nicolin et al, 2011). For the CPU parallel code, calculations involving 10 8 atoms and 10 5 points in reciprocal space are still timeconsuming (Chen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

GAPD: a GPU-accelerated atom-based polychromatic diffraction simulation code

Wang

Chen

Zhang

et al. 2018

J Synchrotron Radiat

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GAPD, a graphics-processing-unit (GPU)-accelerated atom-based polychromatic diffraction simulation code for direct, kinematics-based, simulations of X-ray/electron diffraction of large-scale atomic systems with mono-/polychromatic beams and arbitrary plane detector geometries, is presented. This code implements GPU parallel computation via both real- and reciprocal-space decompositions. With GAPD, direct simulations are performed of the reciprocal lattice node of ultralarge systems (∼5 billion atoms) and diffraction patterns of single-crystal and polycrystalline configurations with mono- and polychromatic X-ray beams (including synchrotron undulator sources), and validation, benchmark and application cases are presented.

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“…In addition to the above, an open source Python library entitled PyNX [4] has been recently released to help with a more precise computation of GISAXS patterns for disordered or distorted atomic structures. This library utilizes graphics processors (GPUs) to accelerate the computations of scattering events from structures with large numbers of atoms (> 10…”

Section: Related Workmentioning

confidence: 99%

Massively parallel X-ray scattering simulations

Sarje¹,

Li²,

Chourou³

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Although present X-ray scattering techniques can provide tremendous information on the nano-structural properties of materials that are valuable in the design and fabrication of energy-relevant nano-devices, a primary challenge remains in the analyses of such data. In this paper we describe a high-performance, flexible, and scalable Grazing Incidence Small Angle X-ray Scattering simulation algorithm and codes that we have developed on multi-core/CPU and many-core/GPU clusters. We discuss in detail our implementation, optimization and performance on these platforms. Our results show speedups of ∼125x on a Fermi-GPU and ∼20x on a Cray-XE6 24-core node, compared to a sequential CPU code, with near linear scaling on multi-node clusters. To our knowledge, this is the first GISAXS simulation code that is flexible to compute scattered light intensities in all spatial directions allowing full reconstruction of GISAXS patterns for any complex structures and with highresolutions while reducing simulation times from months to minutes.

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Fast computation of scattering maps of nanostructures using graphical processing units

Cited by 38 publications

References 36 publications

PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators

PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators

GAPD: a GPU-accelerated atom-based polychromatic diffraction simulation code

Massively parallel X-ray scattering simulations

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