As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding evolutionary algorithms (EAs) to turn into parallel EAs (PEAs) and ...
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As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding evolutionary algorithms (EAs) to turn into parallel EAs (PEAs) and to be able to exploit the power of multicores. Parallel computing is a powerful way to reduce the computation time and to improve the quality of EAs solutions. To the stochastic nature of EAs, the known variability of the parallel programs execution times on multicores adds more complexity on PEAs performance evaluations. Performance evaluation methodologies need to adequately deal with the non-determinism in the experimental set. To obtain correct conclusions it is necessary to apply rigorous statistical procedures. The usual estimation of the speedup of a parallel program as the ratio of the sequential execution time and the parallel execution time may not be appropriated if some care is not taken. A correct estimation of the speedup as a performance measure is presented. A method based on the factorial experimental design is proposed to identify which are the significant factors on the performance of a PEA executed on a multicore processor. A case study of the performance analysis of a PEA solving a benchmark test function is presented.
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