  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
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.
