Parallelisation is becoming more and more important as the single core performance increase is stagnating while the amount of cores is increasing with every new generation of hardware. ŒThe traditional approach of manual parallelisation has an alternative in parallel frameworks, such as OpenMP, which can simplify the creation of parallel code. Optimising this code can, however, be cumbersome and difficult. Automating the optimisation or tuning of parallel code and computations is a very interesting alternative to manually optimising algorithms and programs. Previous work has shown that intricate systems can eff‚ectively autotune parallel programs with potentially the same eff‚ectiveness as human experts. ThŒis study suggests using an approach with the main algorithm used inspired from the congestion control algorithms from computer networks, namely AIMD. By applying the algorithm on top of an OpenMP program the parallel parameters such as grain size can be controlled. TheŒe simpli€ed algorithm is shown to be able to achieve a 19% speedup compared to a naive static parallel implementation