The Community for Technology Leaders
2012 IEEE International Conference on Cluster Computing (2012)
Beijing, China China
Sept. 24, 2012 to Sept. 28, 2012
ISBN: 978-1-4673-2422-9
pp: 420-428
Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing amount of algorithms of higher complexity. Parallel programming for data-intensive applications like massive remote sensing image processing on parallel systems is bound to be especially trivial and challenging. We propose a C++ template mechanism enabled generic parallel programming skeleton for these remote sensing applications in high performance clusters. It provides both programming templates for distributed RS data and generic parallel skeletons for RS algorithms. Through one-side communication primitives provided by MPI, the distributed RS data template could provide a global view of the big RS data whose sliced data blocks are scattered among the distributed memory of cluster nodes. Moreover, by data serialization and RMA (Remote Memory Access), the data templates could also offer a simple and effective way to distribute and communicate massive remote sensing data with complex data structures. Furthermore, the generic parallel skeletons implement the recurring patterns of computation, performance optimization and pass the user-defined sequential functions as parameters of templates for type genericity. With the implemented skeletons, Developers without extensive parallel computing technologies can implement efficient parallel remote sensing programs without concerning for parallel computing details. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient.
Remote sensing, Distributed databases, Skeleton, Clustering algorithms, Data processing, Parallel processing, Parallel programming, remote sensing image processing, parallel programming, generic programming, data-intensive computing

Y. Ma, L. Wang, D. Liu, P. Liu, J. Wang and J. Tao, "Generic Parallel Programming for Massive Remote Sensing Data Processing," 2012 IEEE International Conference on Cluster Computing(CLUSTER), Beijing, China China, 2012, pp. 420-428.
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