Daino: A High-level Framework for Parallel and Efficient AMR on GPUs

Mohamed Wahib, Naoya Maruyama, Takayuki Aoki
RIKEN Advanced Institute for Computational Science, Kobe, Japan
SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis 2016, Salt Lake City, UT



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Adaptive Mesh Refinement methods reduce computational requirements of problems by increasing resolution for only areas of interest. However, in practice, efficient AMR implementations are difficult considering that the mesh hierarchy management must be optimized for the underlying hardware. Architecture complexity of GPUs can render efficient AMR to be particularity challenging in GPU-accelerated supercomputers. This paper presents a compiler-based high-level framework that can automatically transform serial uniform mesh code annotated by the user into parallel adaptive mesh code optimized for GPU-accelerated supercomputers. We also present a method for empirical analysis of a uniform mesh to project an upper- bound on achievable speedup of a GPU-optimized AMR code. We show experimental results on three production applications. The speedups of code generated by our framework are comparable to hand-written AMR code while achieving good and weak scaling up to 1000 GPUs.
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