Spatial data processing with MapReduce

Abstract

The current development of high performance parallel supercomputing infrastructures are pushing the boundaries of applications of science and are bringing new paradigms into engineering practices and simulations. Earthquake engineering is also one of the major fields, which benefits from above by looking for solutions in grid computing and cloud computing techniques. Generally, earthquake simulations involve analysis of petabytes of data. Analyzing these large amounts of data in parallel in thousands of nodes in computer clusters results in gaining high performances. Open source cloud solutions such as Hadoop MapReduce, which is highly scalable and capable of processing large amount of data rapidly in parallel on large clusters provide better solution compared to RDBDM. Both GPUs and MapReduce are designed to support vast data parallelism. For performance considerations, GPU computing could be adopted over low performing CPU systems. This paper discusses MapReduce system using Hadoop and Mars. Mars is a MapReduce framework on graphics processor. Hence, the proposition is to use GPU based systems for earthquake simulations in which Digital elevation model 3D data sets are fully materialized where scientist can make use of these data for various analysis and simulations.