Accelerating Particle Swarm Algorithm with GPGPU

This paper focuses on solving large size optimization problems using GPGPU. Evolutionary Algorithms for solving these optimization problems suffer from the curse of dimensionality, which implies that their performance deteriorates as quickly as the dimensionality of the search space increases. This difficulty makes very challenging the performance studies for very high dimensional problems. Furthermore, these studies deal with a limited time-budget. The availability of low cost powerful parallel graphics cards has stimulated the implementation of diverse algorithms on Graphics Processing Units (GPU). In this paper, the design of a GPGPU-based Parallel Particle Swarm Algorithm, to tackle this type of problem maintaining a limited execution time budget, is described. This implementation profits of an efficient mapping of the data elements (swarm of very high dimensional particles) to the parallel processing elements of the GPU. In this problem, the fitness evaluation is the most CPU-costly routine, and therefore the main candidate to be implemented on GPU. As main conclusion, the speed-up curve versus the increase in dimensionality is shown. This curve indicates an asymptotic limit stemmed from the data-parallel mapping.