The aim of the paper is to introduce general techniques in order to optimize the parallel execution time of sorting on a distributed architectures with processors of various speeds. Such an application requires a partitioning step. For uniformly related processors (processors speeds are related by a constant factor), we develop a constant time technique for mastering processor load and execution time in an heterogeneous environment and also a technique to deal with unknown cost functions. For non uniformly related processors, we use a technique based on dynamic programming. Most of the time, the solutions are in O(p) (p is the number of processors), independent of the problem size n. Consequently, there is a small overhead regarding the problem we deal with but it is inherently limited by the knowing of time complexity of the portion of code following the partitioning.
@article{arxiv.cs/0607041,
title = {Methods for Partitioning Data to Improve Parallel Execution Time for Sorting on Heterogeneous Clusters},
author = {Christophe Cérin and Jean-Christophe Dubacq and Jean-Louis Roch and the SafeScale Collaboration},
journal= {arXiv preprint arXiv:cs/0607041},
year = {2016}
}