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A ROBUST PARALLEL FRAMEWORK FOR MASSIVE SPATIAL DATA PROCESSING ON HIGH PERFORMANCE CLUSTERS
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Massive spatial data requires considerable computing power for real-time processing. With the help of the development of multicore technology and computer component cost reduction in recent years, high performance clusters become the only economically viable solution for this requirement. Massive spatial data processing demands heavy I/O operations however, and should be characterized as a data-intensive application. Data-intensive application parallelization strategies are imcompatible with
doi:10.5194/isprsarchives-xxxix-b4-213-2012
fatcat:ksdx4fh3ybhpnc37qygcddrcuu