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

机译:在GPU上的高阶元素的数值集成的实现

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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.
机译:本文介绍了在具有有限数量的硬件上实现资源消耗算法的方法,这是GPU。选择高阶有限元近似的数值积分作为示例算法。为了执行计算测试,我们使用非线性几何元素并解决对流扩散反应问题。对于计算,使用基于开普勒架构和基于Tahiti XT架构的radeon R9 280x的Tesla K20M显卡。将计算实验结果与两个GPU的理论表现进行了比较,这允许评估实际性能。我们的研究提出了选择算法的最佳设计以及这种资源苛刻任务的右硬件的建议。

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