Numerically solving high dimensional partial differential equations(PDEs) is computationally difficult due to the curse of dimensionality. The sparse grid combination technique partially mitigates this problem. A highly parallel framework implementing this technique is the distributed combigrid module of SG++ which is currently only parallelized with message passing. Combining message passing with shared memory parallelization often yields better performance. In this thesis shared-memory parallelization is added to the distributed combigrid module. This allows users of the framework to use hybrid parallelization in their pde-solvers. Speedups up to 3 were measured, in many cases moderate speedups can be seen but for some cases minor slowdowns occurred in comparison to the original version. These slowdowns should still enable using hybrid parallelization for users of the framework and make it worthwhile.     «

Numerically solving high dimensional partial differential equations(PDEs) is computationally difficult due to the curse of dimensionality. The sparse grid combination technique partially mitigates this problem. A highly parallel framework implementing this technique is the distributed combigrid module of SG++ which is currently only parallelized with message passing. Combining message passing with shared memory parallelization often yields better performance. In this thesis shared-memory paralle...     »