The performance of a High Performance Parallel or Distributed Computation
depends heavily on minimizing  communication between the cooperating
processes. Communication is the overhead cost in parallel computing. When
concurrent processes possibly residing on different processors require
access to the same data, delay is introduced as processes need to access
data remotely. In general, programmers of parallel computations have few
tools to help optimize performance. Many of our results can be applied and
automated into program development and real-time control. First, our
research focuses on minimizing the interprocess communication. We develop
several algorithms as an aid to the assignment of data. Second, we define
conditions for data migration between processes at run time. The algorithm
presented can be used as a part of a runtime optimization system. Third,
we explore the Fork-Join construct with respect to performance as a
function of data communication. Fourth, we develop a general framework for
performance modeling in data-parallel cascading Fork-Joins. Lastly, we
apply our data assignment and communication optimization techniques to
parallel LU decomposition. We validate the accuracy of our performance
model, and show how it outperforms conventional approaches.