<span class="var-sub_title">Accelerating Wave-Propagation Algorithms with Adaptive Mesh Refinement Using the Graphics Processing Unit (GPU)</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Accelerating Wave-Propagation Algorithms with Adaptive Mesh Refinement Using the Graphics Processing Unit (GPU)


Authors: Xinsheng Qin (University of Washington), Randall LeVeque (University of Washington), Michael Motley (University of Washington)

Abstract: Clawpack is a library for solving nonlinear hyperbolic partial differential equations using high-resolution finite volume methods based on Riemann solvers and limiters. It supports Adaptive Mesh Refinement (AMR), which is essential in solving multi-scale problems. Recently, we added capabilities to accelerate the code by using the Graphics Process Unit (GPU). Routines that manage CPU and GPU AMR data and facilitate the execution of GPU kernels are added. Customized and CPU thread-safe memory managers are designed to reduce the overhead of memory allocation and de-allocation. Some small GPU kernels are merged into bigger kernels, which greatly reduces kernel launching overhead. A speed-up between 2 and 3 for the total running time is observed in an acoustics benchmark problem. Other Riemann solvers can easily be plugged in and get accelerated. The influence of some AMR parameters and challenges introduced by AMR are also discussed.

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