In the last decade, graphics processing units (GPUs) became a major factor to increase performance in the area of high performance computing. To exploit the full computational power of GPUs, numerous challenges have to be tackled in the area of parallel programming. In this work, we present approaches to benefit from systems ranging from single-GPU setups to large-scale heterogeneous clusters for three different scientific computing applications, each covering different algorithmic characteristics.
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In the last decade, graphics processing units (GPUs) became a major factor to increase performance in the area of high performance computing. To exploit the full computational power of GPUs, numerous challenges have to be tackled in the area of parallel programming. In this work, we present approaches to benefit from systems ranging from single-GPU setups to large-scale heterogeneous clusters for three different scientific computing applications, each covering different algorithmic characteristi...
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