NDLI: Data-centric GPU-based adaptive mesh refinement

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Data-centric GPU-based adaptive mesh refinement

Content Provider	ACM Digital Library
Author	Maruayama, Naoya Wahib, Mohamed
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.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450340014
DOI	10.1145/2833179.2833181
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Adaptive mesh refinement Gpu Cuda
Content Type	Text
Resource Type	Article

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