A Stencil framework to realize large-scale computations beyond device memory capacity on GPU supercomputers

Shimokawabe, Takashi*; Endo, Toshio*; Onodera, Naoyuki; Aoki, Takayuki*

Proceedings of 2017 IEEE International Conference on Cluster Computing (IEEE Cluster 2017) (Internet), p.525 - 529, 2017/09

Stencil-based applications such as CFD have succeeded in obtaining high performance on GPU supercomputers. The problem sizes of these applications are limited by the GPU device memory capacity, which is typically smaller than the host memory. On GPU supercomputers, a locality improvement technique using temporal blocking method with memory swapping between host and device enables large computation beyond the device memory capacity. Our high-productivity stencil framework automatically applies temporal blocking to boundary exchange required for stencil computation and supports automatic memory swapping provided by a MPI/CUDA wrapper library. The framework-based application for the airflow in an urban city maintains 80% performance even with the twice larger than the GPU memory capacity and have demonstrated good weak scalability on the TSUBAME 2.5 supercomputer.