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Parallel DEM Preprocessing Algorithm with Granularity Control on Gridded Terrain Datasets

Jiang Lin

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Abstract

Exiting parallel DEM preprocessing algorithms that do not consider parallel granularity.This paper presents a parallel DEM preprocessing algorithm with granularity control based on the analysis of the sequential algorithm proposed by Moran and Vezina(MV algorithm).A Message Passing Interface(MPI)library is applied to implement the parallel algorithm.The parallel performance of the proposed algorithm is assessed by two gridded DEMs with different sizes on a multi-node Linux cluster.The application results show that the parallel MV algorithm can complete the computing tasks when filling sinks and removing flat areas at any granuality,and it outputs an optimal granularity to achieve the best parallel performance for a given DEM dataset.

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What this paper is about

Exiting parallel DEM preprocessing algorithms that do not consider parallel granularity.This paper presents a parallel DEM preprocessing algorithm with granularity control based on the analysis of the sequential algorithm proposed by Moran and Vezina(MV algorithm).A Message Passing Interface(MPI)library is applied to implement the parallel algorithm.The parallel performance of the proposed algorithm is assessed by two gridded DEMs with different sizes on a multi-node Linux cluster.The application results show that the parallel MV algorithm can complete the computing tasks when filling sinks and removing flat areas at any granuality,and it outputs an optimal granularity to achieve the best parallel performance for a given DEM dataset.

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Available abstract

Exiting parallel DEM preprocessing algorithms that do not consider parallel granularity.This paper presents a parallel DEM preprocessing algorithm with granularity control based on the analysis of the sequential algorithm proposed by Moran and Vezina(MV algorithm).A Message Passing Interface(MPI)library is applied to implement the parallel algorithm.The parallel performance of the proposed algorithm is assessed by two gridded DEMs with different sizes on a multi-node Linux cluster.The application results show that the parallel MV algorithm can complete the computing tasks when filling sinks and removing flat areas at any granuality,and it outputs an optimal granularity to achieve the best parallel performance for a given DEM dataset.

Key concepts: Granularity, Computer science, Preprocessor, Parallel computing, Parallel algorithm, Algorithm, Message Passing Interface, Parallel processing

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