Evaluation of MapReduce for Gridding LIDAR Data

-- 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 sharednothing 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.

• Type: Journal
• Year: 2010
• Where: CLOUDCOM
• Authors: Sriram Krishnan, Chaitanya K. Baru, Christopher J. Crosby
• Keywords: Algorithm, CLOUDCOM 2010, Distributed And Parallel Computing, Implementation, MapReduce