A multi-GPU implementation of a Cellular Genetic Algorithm

In this paper, we present a novel implementation of a Cellular Genetic Algorithm (cGA) model for a multi-GPU platform using NVIDIA's CUDA technology. This multi-GPU cGA model is compared first against a serial version in CPU and then versus an implementation on a single GPU. We divide the different operations of the cGA into distinct sets of instructions called kernels. Using the multi-GPU platform we observe that the speedup with respect to the CPU version ranges from 8 to 771, while it is similar to that of the GPU, with a little overhead in the multi-GPU case. Our results demonstrate that multi-GPU desktops can serve as cost-effective parallel computing platforms to obtain accurate results in very short time, although they need special considerations in order to improve on regular single GPUs.