Context. Agent based models are used to simulate complex systems by using multiple agents that follow a set of rules. One such model is the boid model which is used to simulate movements of synchronized groups of animals. Executing agent based models partially or fully on the GPU has previously shown to increase performance, opening up the possibility for larger simulations. However, few articles have previously compared a full GPU implementation of the boid model with a multi-threaded CPU implementation.

Objectives. The objectives of this thesis are to find how parallel execution of boid model performs when executed on the CPU and GPU respectively, based on the variables frames per second and average boid computation time per frame.

Methods. A performance benchmark experiment will be set up where three implementations of the boid model are implemented and tested.

Results. The collected data is summarized in both tables and graphs, showing the result of the experiment for frames per second and average boid computation time per frame. Additionally, the average results are summarized in two tables.

Conclusions. For the largest flock size the GPGPU implementation performs the best with an average FPS of 42 times over the single-core implementation while the multi-core implementation performs with an average FPS 6 times better than the single-core implementation. For the smallest flock size the single-core implementation is most efficient while the GPGPU implementation has 1.6 times slower average update time and the multi-cor eimplementation has an average update time of 11 times slower compared to the single-core implementation.