Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

doi:10.1177/1094342015572030

Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

2015-02-23

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DOI：10.1177/1094342015572030

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The classical method of determining the atomic structure of complex molecules by analyzing diffraction patterns is currently undergoing drastic developments. Modern techniques for producing extremely bright and coherent X-ray lasers allow a beam of streaming particles to be intercepted and hit by an ultrashort high-energy X-ray beam. Through machine learning methods the data thus collected can be transformed into a three-dimensional volumetric intensity map of the particle itself. The computational complexity associated with this problem is very high such that clusters of data parallel accelerators are required. We have implemented a distributed and highly efficient algorithm for the inversion of large collections of diffraction patterns targeting clusters of hundreds of GPUs. With the expected enormous amount of diffraction data to be produced in the foreseeable future, this is the required scale to approach real-time processing of data at the beam site. Using both real and synthetic data we look at the scaling properties of the application and discuss the overall computational viability of this exciting and novel imaging technique.

Keywords:

Expectation-maximization

X-ray laser diffraction

GPU cluster

single-molecule imaging

Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

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