This paper presents a new approach for optimizing communication of data
parallel programs. Our techniques are based on unidirectional bit-vector
data flow analyses that enable vectorizing, coalescing and aggregating
communication, and overlapping communication with computation both within
and across loop nests. Previous techniques are based on fixed communication
optimization strategies whose quality is very sensitive to changes of machine
and problem sizes. Our algorithm is novel in that we carefully examine
tradeoffs between enhancing communication latency hiding and reducing the
number and volume of messages by systematically evaluating a reasonable set
of promising communication placements for a given program covering several
(possibly conflicting) communication guiding profit motives. We use P3T, a
state-of-the-art performance estimator, to ensure communication buffer-safety
and to find the best communication placement of all created ones. First
results show that our method implies a significant reduction in communication
costs and demonstrate the effectiveness of this analysis in improving the
performance of programs.