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Evaluation of MapReduce for Gridding LIDAR Data

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3 Author(s)
Krishnan, S. ; San Diego Supercomput. Center, UC San Diego, La Jolla, CA, USA ; Baru, C. ; Crosby, C.

The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using shared-nothing parallelism. In this paper, we investigate the use of MapReduce technology for a local gridding algorithm for the generation of Digital Elevation Models (DEM). The local gridding algorithm utilizes the elevation information from LIDAR (Light, Detection, and Ranging) measurements contained within a circular search area to compute the elevation of each grid cell. The method is data parallel, lending itself to implementation using the MapReduce model. Here, we compare our initial C++ implementation of the gridding algorithm to a MapReduce-based implementation, and present observations on the performance (in particular, price/performance) and the implementation complexity. We also discuss the applicability of MapReduce technologies for related applications.

Published in:

Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on

Date of Conference:

Nov. 30 2010-Dec. 3 2010