  This work introduces a runtime model for managing communication with support
for latency-hiding. The model enables non-computer science researchers to
exploit communication latency-hiding techniques seamlessly. For compiled
languages, it is often possible to create efficient schedules for
communication, but this is not the case for interpreted languages. By
maintaining data dependencies between scheduled operations, it is possible to
aggressively initiate communication and lazily evaluate tasks to allow maximal
time for the communication to finish before entering a wait state. We implement
a heuristic of this model in DistNumPy, an auto-parallelizing version of
numerical Python that allows sequential NumPy programs to run on distributed
memory architectures. Furthermore, we present performance comparisons for eight
benchmarks with and without automatic latency-hiding. The results shows that
our model reduces the time spent on waiting for communication as much as 27
times, from a maximum of 54% to only 2% of the total execution time, in a
stencil application.
