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International Journal of Reconfigurable Computing
Volume 2009, Article ID 453970, 14 pages
http://dx.doi.org/10.1155/2009/453970
Research Article

A Decentralised Task Mapping Approach for Homogeneous Multiprocessor Network-On-Chips

1Digital Technology Lab, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany
2Laboratoire d'Informatique, de Robotique et de Microélectroniqe de Montpellier (LIRMM), University of Montpellier II, UMR CNRS 5506, 161 rue ADA, 34392 Montpellier Cedex 5, France
3Institute of Microelectronic Systems, Darmstadt University of Technology , Karlstrasse 15, 64283 Darmstadt, Germany

Received 27 December 2008; Accepted 25 May 2009

Academic Editor: Michael Huebner

Copyright © 2009 Peter Zipf et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a heuristic algorithm for the run-time distribution of task sets in a homogeneous Multiprocessor network-on-chip. The algorithm is itself distributed over the processors and thus can be applied to systems of arbitrary size. Also, tasks added at run-time can be handled without any difficulty, allowing for inline optimisation. Based on local information on processor workload, task size, communication requirements, and link contention, iterative decisions on task migrations to other processors are made. The mapping results for several example task sets are first compared with those of an exact (enumeration) algorithm with global information for a 3 × 3 processor array. The results show that the mapping quality achieved by our distributed algorithm is within 25% of that of the exact algorithm. For larger array sizes, simulated annealing is used as a reference and the behaviour of our algorithm is investigated. The mapping quality of the algorithm can be shown to be within a reasonable range (below 30% mostly) of the reference. This adaptability and the low computation and communication overhead of the distributed heuristic clearly indicate that decentralised algorithms are a favourable solution for an automatic task distribution.