high performance computing on graphics processing units: hgpu.org

This paper develops and evaluates search and optimization techniques for auto-tuning 3D stencil (nearest-neighbor) computations on GPUs. Observations indicate that parameter tuning is necessary for heterogeneous GPUs to achieve optimal performance with respect to a search space. Our proposed framework takes a most concise specification of stencil behavior from the user as a single formula, auto-generates tunable code from it, systematically searches for the best configuration and generates the code with optimal parameter configurations for different GPUs. This auto-tuning approach guarantees adaptive performance for different generations of GPUs while greatly enhancing programmer productivity. Experimental results show that the delivered floating point performance is very close to previous handcrafted work and outperforms other autotuned stencil codes by a large margin. Furthermore, heterogeneous GPU clusters are shown to exhibit the highest performance for dissimilar tuning parameters leveraging proportional partitioning relative to single-GPU performance.