Optimal Scheduling Algorithm for Distributed-Memory Machines
IEEE Transactions on Parallel and Distributed Systems
Stochastic performance prediction for iterative algorithms in distributed environments
Journal of Parallel and Distributed Computing
Massively parallel computing using commodity components
Parallel Computing - Parallel computing on clusters of workstations
Data Locality Exploitation in the Decomposition of Regular Domain Problems
IEEE Transactions on Parallel and Distributed Systems
Matrix Multiplication on Heterogeneous Platforms
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
A software architecture for user transparent parallel image processing
Parallel Computing - Parallel computing in image and video processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
A dynamic earth observation system
Parallel Computing - Special issue: High performance computing with geographical data
Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms
IEEE Transactions on Parallel and Distributed Systems
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
The Journal of Supercomputing
On performance analysis of heterogeneous parallel algorithms
Parallel Computing
Journal of Parallel and Distributed Computing
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
International Journal of High Performance Computing Applications
Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery
International Journal of High Performance Computing Applications
A theoretical approach to the use of cyberinfrastructure in geographical analysis
International Journal of Geographical Information Science
Journal of Signal Processing Systems
Clusters versus GPUs for parallel target and anomaly detection in hyperspectral images
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Hi-index | 0.00 |
The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA's Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.