We introduce a technique for lowering the communication cost in a certain type of distributed application, in which processors perform computation in each time step and must obtain boundary data from their neighbors before they can perform the next time step. A typical example of such an application is solving differential equations using the finite difference method. Our method, which we call SuperBoundary Exchange, consists of sending a larger boundary less often. This results in less frequent data exchange, trading off against larger messages and some redundant computation. We present experimental data showing that our method consistently results in significant speedup in communication-intensive applications, under varying assumption about the balance of computation load among the processors.

We describe an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigm-oriented distributed computing is that the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelization and distribution. The main features of the proposed approach, called PODC, which differentiate it from other approaches, are the following: (1) It is intended for loosely-coupled network environments, not specialized multiprocessors; (2) it is based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a functional or special-purpose language, and (4) it provides a Web-based interactive graphics interface through which programs are constructed, invoked, and monitored.

The three paradigms presently supported in PODC are the bag-of-tasks, the branch-and-bound, and genetic programming. We demonstrate their use, implementation, and performance within the mobile agent-based PODC environment.