Parallel processing strategies for geospatial data in a cloud computing infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading was hereby assessed.

KEMPENEERS Pieter;  KLIMENT Tomas;  MARLETTA Luca;  SOILLE Pierre; 

2022-02-11

MDPI

JRC127028

2072-4292 (online),   

https://www.mdpi.com/2072-4292/14/2/398,    https://publications.jrc.ec.europa.eu/repository/handle/JRC127028,   

10.3390/rs14020398 (online),