Adaptively Mapping Parallelism Based on System Workload Using Machine Learning

Parallel Computing has become pervasive and the number of processors placed in computers will further increase in the future. However, software developers are struggling to efficiently exploit the computational resources provided by parallel architectures. It is thus inevitable to investigate the behaviour of parallel programs and develop methods that help improving their performance. The software developer should not have to worry about how to map parallelism to the underlying architecture, but should instead concentrate on exposing the parallelism and leave the mapping task to the runtime system. In this project, the behaviour of parallel programs in the presence of workload is investigated. It is shown that choosing the right number of threads for an application is crucial to achieve the best performance possible when there is other workload running on the system. The default policy of creating as many threads as there are cores is rarely optimal in this situation and using the optimal number of threads reduces the runtime by 22.5% on average w.r.t. the default policy. Determining the optimal number of threads is not a straight-forward task, because it not only depends on the current workload but also on the program itself. For some programs, reducing the number of threads w.r.t. the default yields the optimal solution, whereas for other programs the best performance is achieved using more threads than there are cores. In order to tackle this problem, a novel technique for choosing the number of threads is presented in this work. Using Machine Learning techniques, a model is created that predicts the optimal number of threads based on the current system workload and the program. Different approaches for modelling this problem and several sets of features are evaluated. With the best model, a speedup of 92% of the optimal performance is achieved, which corresponds to a runtime reduction of almost 16% over the default policy.