Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
Parallel image processing applications on a network of workstations
Parallel Computing
Wide-area implementation of the message passing interface
Parallel Computing - Special issue on applications
Assessing Fast Network Interfaces
IEEE Micro
Digital Image Processing: A 1996 Review
PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
Distributed Georeferring of Remotely Sensed Landsat-TM Imagery Using MPI
PARA '98 Proceedings of the 4th International Workshop on Applied Parallel Computing, Large Scale Scientific and Industrial Problems
Analyzing the Performance of MPI in a Cluster of Workstations Based on Fast Ethernet
Proceedings of the 4th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
MPI on NT: The Current Status and Performance of the Available Environments
Proceedings of the 5th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Computer Processing of Remotely-Sensed Images: An Introduction
Computer Processing of Remotely-Sensed Images: An Introduction
Parallel processing with windows NT networks
NT'97 Proceedings of the USENIX Windows NT Workshop on The USENIX Windows NT Workshop 1997
Evaluating the DIPORSI Framework: Distributed Processing of Remotely Sensed Imagery
Proceedings of the 8th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
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The design of efficient distributed applications depends on the coordinate use of different API (Application Programming Interface) like MPI and NT API's. In fact, a particular optimized code can be reused in many other applications reducing the cost of its design by means of a set of libraries. Distributed processing is applied in remote sensing in order to reduce spatial or temporal cost using the message passing paradigm. In this paper, we present a workbench called DIPORSI, developed to provide a framework for the distributed processing of Landsat images using a cluster of NT workstations. Our application is based on a NT implementation (WMPI) of the MPI standard. Thus, the large amount of time required by the sequential processes drops when the parallel processing is used. Moreover, we have obtained a reduction of computation time over the 400% for large size images and a moderate number of parallel nodes. Our results confirm that cluster computing is a cost/performance effective solution to the remotely sensed image processing.