Massively parallel computing using commodity components
Parallel Computing - Parallel computing on clusters of workstations
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
MODTRAN on supercomputers and parallel computers
Parallel Computing
MPI: The Complete Reference
Adaptive parallel computing on heterogeneous networks with mpC
Parallel Computing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
A distributed spectral-screening PCT algorithm
Journal of Parallel and Distributed Computing
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
HeteroMPI: Towards a message-passing library for heterogeneous networks of computers
Journal of Parallel and Distributed Computing
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 2
ISPDC '06 Proceedings of the Proceedings of The Fifth International Symposium on Parallel and Distributed Computing
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
High Performance Computing in Remote Sensing
High Performance Computing in Remote Sensing
Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit
International Journal of High Performance Computing Applications
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The development of efficient techniques for transforming massive volumes of remotely sensed hyperspectral data into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for information extraction and mining from hyperspectral imagery assume homogeneity in the underlying computing platform, heterogeneous networks of computers (HNOCs) have become a promising cost-effective solution, expected to play a major role in many on-going and planned remote sensing missions. In this paper, we develop a new morphological parallel algorithm for hyperspectral image classification using HeteroMPI, an extension of MPI for programming high-performance computations on HNOCs. The main idea of HeteroMPI is to automate and optimize the selection of a group of processes that executes a heterogeneous algorithm faster than any other possible group in a heterogeneous environment. In order to analyze the impact of many-to-one (gather) communication operations introduced by our proposed algorithm, we resort to a recently proposed collective communication model. The parallel algorithm is validated using two heterogeneous clusters at University College Dublin and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center.