کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
432420 688884 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient asynchronous executions of AMR computations and visualization on a GPU system
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Efficient asynchronous executions of AMR computations and visualization on a GPU system
چکیده انگلیسی


• Asynchronous execution between CPU and GPU is improved.
• Redundant computations found in the default scheme are eliminated.
• The order of computations is changed to get the high priority result first.
• Visualization of the high priority data is overlapped with the computation of the remaining portions.

Adaptive Mesh Refinement is a method which dynamically varies the spatio-temporal resolution of localized mesh regions in numerical simulations, based on the strength of the solution features. In-situ visualization plays an important role for analyzing the time evolving characteristics of the domain structures. Continuous visualization of the output data for various timesteps results in a better study of the underlying domain and the model used for simulating the domain. In this paper, we develop strategies for continuous online visualization of time evolving data for AMR applications executed on GPUs. We reorder the meshes for computations on the GPU based on the users input related to the subdomain that he wants to visualize. This makes the data available for visualization at a faster rate. We then perform asynchronous executions of the visualization steps and fix-up operations on the CPUs while the GPU advances the solution. By performing experiments on Tesla S1070 and Fermi C2070 clusters, we found that our strategies result in 60% improvement in response time and 16% improvement in the rate of visualization of frames over the existing strategy of performing fix-ups and visualization at the end of the timesteps.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Parallel and Distributed Computing - Volume 73, Issue 6, June 2013, Pages 866–875
نویسندگان
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