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An improved coarse-grained parallel algorithm for computational acceleration of ordinary Kriging interpolation

机译：改进的粗粒并行算法，用于普通克里格插值的计算加速

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Heavy computation limits the use of Kriging interpolation methods in many real-time applications, especially with the ever-increasing problem size. Many researchers have realized that parallel processing techniques are critical to fully exploit computational resources and feasibly solve computation-intensive problems like Kriging. Much research has addressed the parallelization of traditional approach to Kriging, but this computation-intensive procedure may not be suitable for high-resolution interpolation of spatial data. On the basis of a more effective serial approach, we propose an improved coarse-grained parallel algorithm to accelerate ordinary Kriging interpolation. In particular, the interpolation task of each unobserved point is considered as a basic parallel unit. To reduce time complexity and memory consumption, the large right hand side matrix in the Kriging linear system is transformed and fixed at only two columns and therefore no longer directly relevant to the number of unobserved points. The MPI (Message Passing Interface) model is employed to implement our parallel programs in a homogeneous distributed memory system. Experimentally, the improved parallel algorithm performs better than the traditional one in spatial interpolation of annual average precipitation in Victoria, Australia. For example, when the number of processors is 24, the improved algorithm keeps speed-up at 20.8 while the speed-up of the traditional algorithm only reaches 93. Likewise, the weak scaling efficiency of the improved algorithm is nearly 90% while that of the traditional algorithm almost drops to 40% with 16 processors. Experimental results also demonstrate that the performance of the improved algorithm is enhanced by increasing the problem size. (C) 2015 Elsevier Ltd. All rights reserved.
机译：繁重的计算限制了克里格插值方法在许多实时应用中的使用，尤其是随着问题规模的不断扩大。许多研究人员已经意识到，并行处理技术对于充分利用计算资源并切实解决诸如Kriging之类的计算密集型问题至关重要。许多研究已经解决了传统克里格方法的并行化问题，但是这种计算量大的过程可能不适合于空间数据的高分辨率插值。在更有效的串行方法的基础上，我们提出了一种改进的粗粒度并行算法来加速普通的克里格插值。特别地，每个未观察点的插值任务被视为基本并行单元。为了减少时间复杂度和内存消耗，对Kriging线性系统中的大型右手侧矩阵进行了转换并固定在仅两列，因此不再与未观测点的数量直接相关。 MPI（消息传递接口）模型用于在同质分布式存储系统中实现并行程序。在实验上，改进的并行算法在澳大利亚维多利亚州的年平均降水量的空间插值方面比传统算法更好。例如，当处理器数量为24时，改进的算法将加速比保持在20.8，而传统算法的加速比仅达到93。同样，改进算法的弱缩放效率接近90％，而改进算法的弱缩放效率接近传统算法在16个处理器的情况下几乎下降到40％。实验结果还表明，改进算法的性能通过增加问题的大小来增强。 （C）2015 Elsevier Ltd.保留所有权利。

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