ABSTRACT

This study aims to employ both central processing units (CPUs) and graphics processing units (GPUs) to collaboratively generate contour lines from a large-scale digital elevation model (DEM). The performance was improved with regard to three aspects. First, the original DEM data were decomposed and assigned according to the GPU’s limited memory so that large-scale data could be correctly addressed. Second, different types of computational tasks between the CPUs and GPUs were dynamically scheduled to ensure that both accelerators cooperate for performance improvement. Third, parallel processing on GPUs and CPUs was separately optimized for more efficient acceleration. Experimental results indicated that applying the parallel algorithm to data with a volume of 37.81 GB and area of 5,975,625.16 km² reduced the total execution time from 332.84 min to 8.29 min for an optimal speedup of 40.15. In addition, we investigated the effects of the computational intensity, decomposition granularity, and task scheduling on parallel efficiency and performance improvement. We also discussed its degree of effectiveness, broader application, and the future direction of research in the field of geographic information systems.

摘要

这项研究旨在利用中央处理单元（CPU）和图形处理单元（GPU）来从大型数字高程模型（DEM）协同生成轮廓线。从三个方面改进了性能。首先，原始的DEM数据将根据GPU的有限内存进行分解和分配，以便可以正确处理大规模数据。其次，对CPU和GPU之间的不同类型的计算任务进行了动态调度，以确保两个加速器共同协作以提高性能。第三，分别优化了GPU和CPU上的并行处理，以实现更高效的加速。实验结果表明，将并行算法应用于容量为37.81 GB，面积为5,975,625.16 km ^2的数据将总执行时间从332.84分钟减少到8.29分钟，以实现40.15的最佳加速。此外，我们研究了计算强度，分解粒度和任务调度对并行效率和性能改进的影响。我们还讨论了其有效性程度，更广泛的应用以及地理信息系统领域的未来研究方向。