Modular Mappings and Data Distribution Independent Computations

Abstract

This paper considers the problem of writing data distribution independent (DDI) programs in order to eliminate or reduce initial data redistribution overheads for distributed memory parallel computers. The functionality and execution time of DDI programs are independent of initial data distributions. Modular mappings, which can be used to derive many equally optimal and functionally equivalent programs, are briefly reviewed. Relations between modular mappings and input data distributions are then established. These relations are the basis of a systematic approach to the derivation of DDI programs which is illustrated for matrix-matrix multiplication (c = a × b). Conditions of data distributions for which it is possible to find a modular mapping that yields a programa as efficient as Cannon's algorithm are: (1) the first row of the inverse of pattern distribution of array 'a' should be equal to be equal to the second row of the inverse of pattern distribution of array 'b', (2) the second row of the inverse of pattern distribution of array 'a' should be linearly independent of the first row of the inverse of pattern distribution of array 'b', and (3) each pattern distribution of arrays 'a', 'b', and 'c' should have at least one zero entry, respectively.