数据粒度是海量空间数据并行计算的重要问题之一.通过对不同性质的并行算法的对比分析,提出空间数据粒度模型,量化地反映并行地形分析中数据划分的规模,建立并行数据粒度评价模型.通过研究集群环境下不同算法的数据并行数据粒度问题,提出基于并行数据粒度评价模型的优化数据粒度调度算法.通过计算每一次并行计算的时间与数据粒度效率,从而实现对计算数据粒度动态更新以追求更高的加速比.经过实验验证,该算法较之传统算法,可提供更高的任务执行效率并具有更好的可移植性.%Data granularity is one of the most important issues of parallel computing based on large volume of spatial data. After a comparison with the different types of terrain analysis algorithms, a Geo-Data Granularity Model ( GDGM in short) was proposed, which can be used for the quantitative description of data partition granularity in parallel computing process of massive spatial data. In this algorithm, according to the parallel evaluation method, the parallel data granularity was adaptively adjusted and finally an optimized data grain was obtained. The execution time of each parallel computing was recorded for the comparison of computing efficiency values from different data granularities. Furthermore, by means of the comparison, a dynamic algorithm was designed for the dynamic scheduling of different data granularity so that the optimal performance of specific algorithm was achieved. The preliminary experiments show that the algorithm has much better efficiency and portability than the traditional ones so far.
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