Genetic algorithms for parallel code optimization

Determining the optimum data distribution, degree of parallelism and the communication structure on distributed memory machines for a given algorithm is not a straightforward task. Assuming that a parallel algorithm consists of consecutive stages, a genetic algorithm is proposed to find the best number of processors and the best data distribution method to be used for each stage of the parallel algorithm. Steady state genetic algorithm is compared with transgenerational genetic algorithm using different crossover operators. Performance is evaluated in terms of the total execution time of the program including communication and computation times. A computation intensive, a communication intensive and a mixed implementation are utilized in the experiments. The performance of GA provides satisfactory results for these illustrative examples.