Non-uniform and dynamic domain decompositions for hypercomputing

Abstract

Implementing efficient parallel programs on a network-based computing platform is still a challenge. This paper proposes a new adaptive data distribution (ADD) support that avoids the complex task of managing irregular data distributions and adapting them to the variable conditions of a multi-users system. In particular, ADD provides the programmer with a data partition algorithm that fits the non-uniformity of the platform nodes at the beginning of program execution, a set of data inquiry primitives that allow the programmer to deal with a logical regular partition and a runtime support that autonomously adapts the data distribution to the node power variations occurring during computation. Several experimental results demonstrate that ADD is a very useful tool to maintain the efficiency of SPMD computations especially when the platform is highly non-uniform and variable.