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Parallel Computing In Remote Sensing Data Processing

There are a growing number of people who want to use remotely sensed data and GIS data. What is needed is a large-scale processing and storage system that provides high bandwidth at low cost.

Scalable computing clusters, ranging from a cluster of (homogeneous or heterogeneous) PCs or workstations, to SMPs, are rapidly becoming the standard platforms for high-performance and large-scale computing. To utilize the resources of a parallel computer, a problem had to be algorithmically expressed as comprising a set of concurrently executing sub-problems or tasks.

To utilize the parallelism of cluster of SMPs, we present the basic programming techniques by using PVM to implement a message-passing program. The matrix multiplication and parallel ray tracing problems are illustrated and the experiments are also demonstrated on our Linux SMPs cluster. The experimental results show that our Linux/PVM cluster can achieve high speedups for applications.



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