UC Irvine

The divisible load scheduling of image processing applications on the heterogeneous star and multi-level tree networks is addressed in this paper. In our platforms, processors and network links have different speeds. In addition, computation and communication overheads are considered. A new genetic algorithm for minimizing the processing time of low-level image applications using divisible load theory is introduced. The closed-form solution for the processing time, the image fractions that should be allocated to each processor, the optimum number of participating processors, and the optimal sequence for load distribution are derived. The new concept of equivalent processor in tree network is introduced and the effect of different image and kernel sizes on processing time and speed up are investigated. Finally, to indicate the efficiency of our algorithm, several numerical experiments are presented.