Todays commercial off-the-shelf computer systems are multicore computing systems as a combination of CPU, graphic processor (GPU) and custom devices. In comparison with CPU cores, graphic cards are capable to execute hundreds up to thousands compute units in parallel. To benefit from these GPU computing resources, applications have to be parallelized and adapted to the target architecture. In this paper we show our experience in applying the NQueens puzzle solution on GPUs using Nvidia's CUDA (Compute Unified Device Architecture) technology. Using the example of memory usage and memory access, we demonstrate that optimizations of CUDA programs may have contrary results on different CUDA architectures. Evaluation results will point out, that it is not sufficient to use new programming languages or compilers to achieve best results with emerging graphic card computing.

GPU compute devices have become very popular for general purpose computations. However, the SIMD-like hardware of graphics processors is currently not well suited for irregular workloads, like searching unbalanced trees. In order to mitigate this drawback, NVIDIA introduced an extension to GPU programming models called Dynamic Parallelism. This extension enables GPU programs to spawn new units of work directly on the GPU, allowing the refinement of subsequent work items based on intermediate results without any involvement of the main CPU.This work investigates methods for employing Dynamic Parallelism with the goal of improved workload distribution for tree search algorithms on modern GPU hardware. For the evaluation of the proposed approaches, a case study is conducted on the N-Queens problem. Extensive benchmarks indicate that the benefits of improved resource utilization fail to outweigh high management overhead and runtime limitations due to the very fine level of granularity of the investigated problem. However, novel memory management concepts for passing parameters to child grids are presented. These general concepts are applicable to other, more coarse-grained problems that benefit from the use of Dynamic Parallelism.

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