Data-centric GPU-based adaptive mesh refinement

Abstract

It has been demonstrated that explicit stencil computations of high-resolution scheme can highly benefit from GPUs. This includes Adaptive Mesh Refinement (AMR), which is a model for adapting the resolution of a stencil grid locally. Unlike uniform grid stencils, however, adapting the grid is typically done on the CPU side. This requires transferring the stencil data arrays to/from CPU every time the grid is adapted. We propose a data-centric approach to GPU-based AMR. That is, porting all the mesh adaptation operations touching the data arrays to the GPU. This would allow the stencil data arrays to reside on the GPU memory for the entirety of the simulation. Thus, the GPU code would specialize on the data residing on its memory while the CPU specializes on the AMR metadata residing on CPU memory. We compare the performance of the proposed method to a basic GPU implementation and an optimized GPU implementation that overlaps communication and computation. The performance of two GPU-based AMR applications is enhanced by 2.21x, and 2.83x compared to the basic implementation.