  Ptychography is an emerging imaging technique that is able to provide
wavelength-limited spatial resolution from specimen with extended lateral
dimensions. As a scanning microscopy method, a typical two-dimensional image
requires a number of data frames. As a diffraction-based imaging technique, the
real-space image has to be recovered through iterative reconstruction
algorithms. Due to these two inherent aspects, a ptychographic reconstruction
is generally a computation-intensive and time-consuming process, which limits
the throughput of this method. We report an accelerated version of the
multi-mode difference map algorithm for ptychography reconstruction using
multiple distributed GPUs. This approach leverages available scientific
computing packages in Python, including mpi4py and PyCUDA, with the core
computation functions implemented in CUDA C. We find that interestingly even
with MPI collective communications, the weak scaling in the number of GPU nodes
can still remain nearly constant. Most importantly, for realistic diffraction
measurements, we observe a speedup ranging from a factor of $10$ to $10^3$
depending on the data size, which reduces the reconstruction time remarkably
from hours to typically about 1 minute and is thus critical for real-time data
processing and visualization.
