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OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

机译:异构系统上大规模空间数据插值的并行通用Kriging算法的OpenCL实现

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In some digital Earth engineering applications, spatial interpolation algorithms are required to process and analyze large amounts of data. Due to its powerful computing capacity, heterogeneous computing has been used in many applications for data processing in various fields. In this study, we explore the design and implementation of a parallel universal kriging spatial interpolation algorithm using the OpenCL programming model on heterogeneous computing platforms for massive Geo-spatial data processing. This study focuses primarily on transforming the hotspots in serial algorithms, i.e. , the universal kriging interpolation function, into the corresponding kernel function in OpenCL. We also employ parallelization and optimization techniques in our implementation to improve the code performance. Finally, based on the results of experiments performed on two different high performance heterogeneous platforms, i.e. , an NVIDIA graphics processing unit system and an Intel Xeon Phi system (MIC), we show that the parallel universal kriging algorithm can achieve the highest speedup of up to 40× with a single computing device and the highest speedup of up to 80× with multiple devices.
机译:在某些数字地球工程应用中,需要空间插值算法来处理和分析大量数据。由于其强大的计算能力,异构计算已在许多应用程序中用于各个领域的数据处理。在这项研究中,我们探索了在异构计算平台上使用OpenCL编程模型进行并行通用克里金空间插值算法的设计和实现,以用于大规模地理空间数据处理。这项研究主要致力于将串行算法中的热点(即通用克里格插值函数)转换为OpenCL中的相应内核函数。我们还在实现中采用并行化和优化技术来提高代码性能。最后,基于在两个不同的高性能异构平台(即NVIDIA图形处理单元系统和Intel Xeon Phi系统(MIC))上进行的实验结果,我们证明了并行通用克里金算法可以实现最高的加速速度。单个计算设备可达到40倍,而多个设备则可达到80倍。

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