  New techniques in X-ray scattering science experiments produce large data
sets that can require millions of high-performance processing hours per week of
computation for analysis. In such applications, data is typically moved from
X-ray detectors to a large parallel file system shared by all nodes of a
petascale supercomputer and then is read repeatedly as different science
application tasks proceed. However, this straightforward implementation causes
significant contention in the file system. We propose an alternative approach
in which data is instead staged into and cached in compute node memory for
extended periods, during which time various processing tasks may efficiently
access it. We describe here such a big data staging framework, based on MPI-IO
and the Swift parallel scripting language. We discuss a range of large-scale
data management issues involved in X-ray scattering science and measure the
performance benefits of the new staging framework for high-energy diffraction
microscopy, an important emerging application in data-intensive X-ray
scattering. We show that our framework accelerates scientific processing
turnaround from three months to under 10 minutes, and that our I/O technique
reduces input overheads by a factor of 5 on 8K Blue Gene/Q nodes.
