A Comparative Evaluation of the Gpu vs. the Cpu for Parallelization of Evolutionary Algorithms through Multiple Independent Runs

Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the efficiency of parameter tuning or to speed up optimizations involving inexpensive fitness functions. A GPU platform is commonly adopted in the research community to implement parallelization, and this platform has been shown to be superior to the traditional CPU platform in many previous studies. However, it is not clear how efficient the GPU is in comparison with the CPU for the parallelizing multiple independent runs, as the vast majority of the previous studies focus on parallelization approaches in which the parallel runs are dependent on each other (such as master-slave, coarse-grained or fine-grained approaches). This study therefore aims to investigate the performance of the GPU in comparison with the CPU in the context of multiple independent runs in order to provide insights into which platform is most efficient. This is done through a number of experiments that evaluate the efficiency of the GPU versus the CPU in various scenarios. An analysis of the results shows that the GPU is powerful, but that there are scenarios where the CPU outperforms the GPU. This means that a GPU is not the universally best option for parallelizing multiple independent runs and that the choice of computation platform therefore should be an informed decision. To facilitate this decision and improve the efficiency of optimizations involving multiple independent runs, the paper provides a number of recommendations for when and how to use the GPU.