An Efficient Parallel Gpu Evaluation of Small Angle X-Ray Scattering Profiles

Antonov, Lubomir D.; Andreetta, Christian; Hamelryck, Thomas

doi:10.5220/0003781501020108

Cited by 1 publication

(1 citation statement)

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“…As such, GPUs provide the best results for that type of problem and there have been several successful attempts to compute the Debye scattering equation on that type of device. Restricting ourselves to open-source solutions, for which the code can hence be reused and redistributed, one can cite for example the PHAISTOS program (Antonov et al, 2012) and the XaNSoNS program (Neverov, 2017), the latter being partly written in Python. Although we did not test these programs in the present work, it can be expected that better results will be obtained because of the massively parallel architecture of GPUs.…”

Section: Pairwise Distances Summationmentioning

confidence: 99%

High-performance Python for crystallographic computing

Boulle¹,

Kieffer²

2019

J Appl Cryst

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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against stateof-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes. ISSN 1600-5767 teaching and education J. Appl. Cryst. (2019). 52, 882-897 Boulle and Kieffer High-performance Python for crystallographic computing 883 Figure 1Fraction of articles published every year by the International Union of Crystallography containing the word 'Python' in the title or in the abstract.

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Section: Pairwise Distances Summationmentioning

confidence: 99%