Implementation of Numerical Integration to High-Order Elements on the GPUs

Filip Kruzel, K. Banas, M. Nytko
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Abstract

This article presents ways to implement a resource-consuming algorithm on hardware with a limited amount of memory, which is the GPU. Numerical integration for higher-order finite element approximation was chosen as an example algorithm. To perform computational tests, we use a non-linear geometric element and solve the convection-diffusion-reaction problem. For calculations, a Tesla K20m graphics card based on Kepler architecture and Radeon r9 280X based on Tahiti XT architecture were used. The results of computational experiments were compared with the theoretical performance of both GPUs, which allowed an assessment of actual performance. Our research gives suggestions for choosing the optimal design of algorithms as well as the right hardware for such a resource-demanding task.
gpu上高阶元数值积分的实现
本文介绍了在内存有限的硬件(即GPU)上实现资源消耗算法的方法。以高阶有限元逼近的数值积分算法为例。为了进行计算测试,我们使用了非线性几何单元并解决了对流-扩散-反应问题。计算使用基于Kepler架构的Tesla K20m显卡和基于Tahiti XT架构的Radeon r9 280X。计算实验结果与两种gpu的理论性能进行了比较,从而可以对实际性能进行评估。我们的研究为选择算法的最佳设计以及合适的硬件来完成这样一个资源要求很高的任务提供了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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