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Implementation of Cubic Spline Interpolation on Parallel Skeleton Using Pipeline Model on CPU-GPU Cluster

机译:在CPU-GPU集群上使用流水线模型在平行骨架上三次三次样条插值的实现

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The cubic spline interpolation is frequently used for analysis the data set in various aspects of engineering and science problem. For a large set of data points defined with very large range, it is very difficult to interpolate by using traditional sequential algorithm. In this paper, we proposed a more systematic approach which has a parallel component known as skeleton which is implemented in various parallel paradigms like OpenMP, MPI, and CUDA etc. It is interesting that the skeleton approach is used with pipelining technique that gives better result as compared to the previous studies. This approach is applied to compute the cubic spline interpolating polynomial based on a large data set. The experimental result using the parallel skeleton technique on multi-core CPU and GPU shows better performance with respect to other parallel methods.
机译:三次样条插值经常用于分析工程和科学问题各个方面的数据集。对于定义很大范围的大量数据点,很难使用传统的顺序算法进行插值。在本文中,我们提出了一种更具系统性的方法,该方法具有称为骨架的并行组件,可以在各种并行范例(如OpenMP,MPI和CUDA等)中实现。有趣的是,该骨架方法与流水线技术一起使用可提供更好的结果与以前的研究相比。该方法用于基于大数据集计算三次样条插值多项式。与其他并行方法相比,在多核CPU和GPU上使用并行骨架技术的实验结果显示出更好的性能。

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