A framework for automatic dynamic data mapping

Physically-distributed memory multiprocessors are becoming popular and data distribution and loop parallelization are aspects that a parallelizing compiler has to consider in order to get efficiency from the system. The cost of accessing local and remote data can be one or several orders of magnitude different, and this can dramatically affect the performance of the system. It would be desirable to free the programmer from considerations of the low-level details of the target architecture, to program explicit processes or specify interprocess communication. We present an approach to automatically derive static or dynamic data distribution strategies for the arrays used in a program. All the information required about data movement and parallelism is contained in a single data structure, called the Communication-Parallelism Graph (CPG). The problem is modeled and solved using a general purpose linear 0-1 integer programming solver. This allows us to find the optimal solution for the problem for one-dimensional array distributions. We also show the feasibility of using this approach in terms of compilation time and quality of the solutions generated