A systematic parallel strategy for generating contours from large-scale DEM data using collaborative CPUs and GPUs

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.

KEYWORDS:

• Geographical information systems
• digital elevation model
• contour line
• parallel computing
• task scheduling

Acknowledgments

We sincerely thank the editors and anonymous reviewers for their constructive comments, which have helped improve this paper.

Disclosure statement

The authors declare that they have no conflict of interest.

Data availability statement

The data that support the findings of this study are available with the identifier at the link https://doi.org/10.17605/OSF.IO/CM7RF.

Additional information

Funding

This work was supported by the National Key Research & Development Program of China (grant number 2017YFB0504205) and the National Natural Science Foundation of China (grant number 41901318).