An Automatic Computation and Data Decomposition Algorithm of Prioritized Dominant Array

Abstract

Automatic decomposition is a compile technique that maps computation and data onto different processors, and array is one of the main targets processed. Certain arrays, whose migration is the most expensive, are termed as dominant arrays. Since every computing node has its own memory on distributed memory parallel computers (DMPCs), decompositions of dominant arrays have directly impact on the performance of parallel program. To avoid remote data accessing, each definition and use of arrays needs to be distributed consistently, so as that there are too many partition constraints to increase decomposition choices of dominant arrays. We propose an automatic computation and data decomposition algorithm of prioritized dominant array in this paper. Our algorithm ranks arrays according to their potential communication costs, and then finds data decomposition for arrays in the decreasing order of rank. We serialize low rank arrays to enhance the decomposition priority of high rank ones. Finally, serial arrays are to be partitioned if maintaining parallel benefits of previous results. The experimental results show that this algorithm can improve the performance of parallel programs.