  Differential Evolution (DE) is a highly successful population based global
optimisation algorithm, commonly used for solving numerical optimisation
problems. However, as the complexity of the objective function increases, the
wall-clock run-time of the algorithm suffers as many fitness function
evaluations must take place to effectively explore the search space. Due to the
inherently parallel nature of the DE algorithm, graphics processing units (GPU)
have been used to effectively accelerate both the fitness evaluation and DE
algorithm. This work reviews the main architectural choices made in the
literature for GPU based DE algorithms and introduces a new GPU based numerical
optimisation benchmark to evaluate and compare GPU based DE algorithms.
