A High-Productivity Framework for Adaptive Mesh Refinement on Multiple GPUs

Abstract

Recently grid-based physical simulations with multiple GPUs require effective methods to adapt grid resolution to certain sensitive regions of simulations. In the GPU computation, an adaptive mesh refinement (AMR) method is one of the effective methods to compute certain local regions that demand higher accuracy with higher resolution. However, the AMR methods using multiple GPUs demand complicated implementation and require various optimizations suitable for GPU computation in order to obtain high performance. Our AMR framework provides a high-productive programming environment of a block-based AMR for grid-based applications. Programmers just write the stencil functions that update a grid point on Cartesian grid, which are executed over a tree-based AMR data structure effectively by the framework. It also provides the efficient GPU-suitable methods for halo exchange and mesh refinement with a dynamic load balance technique. The framework-based application for compressible flow has achieved to reduce the computational time to less than 15% with 10% of memory footprint in the best case compared to the equivalent computation running on the fine uniform grid. It also has demonstrated good weak scalability with \(84\%\) of the parallel efficiency on the TSUBAME3.0 supercomputer.