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首页> 外文期刊>WSEAS Transactions on Information Science and Applications >Comparison of Design and Performance of Snow Cover Computing on GPUs and Multi-core processors
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Comparison of Design and Performance of Snow Cover Computing on GPUs and Multi-core processors

机译：GPU和多核处理器上的积雪计算设计和性能比较

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The aim of this work is the depth of the snow cover computing in the desired point based on the geographical characteristics of a specific geographical point in a modeled area. The measured data are known on few places. These places are coincident with raingauge stations. These data have been collected by many continuous observations and measurements at the specific climatologic raingauging stations of Slovak Hydrometeorogical Institute. An interpolation method is necessary to obtain a representation of real situation about whole surface. The main characteristic of the interpolation computing is the fact that it is time-consuming. In paper, we present two cheap approaches of HPC. The first solution is a utilization of graphics processing units (GPUs) where the availability of enormous computational performance of easily programmable GPUs can rapidly decrease time of computing. The second one is a utilization of multi-thread CPUs. In our article we demonstrate how to deploy the CUDA architecture, which utilizes the powerful parallel computation capacity of GPU, to accelerate computational process of snow cover depth using the inverse-distance weighting (IDW) method. The performance of GPU we face with OpenMP implementation of IDW method. We consider variable number of threads per CPU. The outputs are visualized by the GIS Grass tool.
机译：这项工作的目的是根据建模区域中特定地理位置的地理特征，在所需点进行积雪计算的深度。测量数据在少数地方是已知的。这些地方与雨量计站相吻合。这些数据是通过斯洛伐克水文气象研究所的特定气候学测量站的许多连续观测和测量收集的。必须使用插值方法来获得有关整个表面的真实情况的表示。插值计算的主要特征是它很耗时。在本文中，我们提出了两种廉价的HPC方法。第一个解决方案是利用图形处理单元（GPU），其中易于编程的GPU的巨大计算性能的可用性可迅速减少计算时间。第二个是利用多线程CPU。在我们的文章中，我们演示了如何部署CUDA架构，该架构利用GPU强大的并行计算能力，使用反距离权重（IDW）方法来加速积雪深度的计算过程。我们用IDW方法的OpenMP实现面对GPU的性能。我们考虑每个CPU的可变线程数。输出通过GIS Grass工具可视化。

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