high performance computing on graphics processing units: hgpu.org

GPU based heterogeneous systems provide a peak performance in the order of TFlop/s and an advantageous ratio between performance and energy consumption. However, reaching high performance on GPUs is often a difficult task. This thesis proposes advanced optimization techniques that allow for efficiently porting a set of sparse grid algorithms to GPUs. The performance obtained on GPUs is improved using an auto-tuning strategy whereas full utilization of the heterogeneous system is ensured using different load balancing schemes.