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In recent years, cluster computing has been accepted widely as a parallel platform because of its high performance at an affordable cost. To make the best use of the cluster computing resources, a resource monitoring program is needed. The information collected can be used by any parallel application, i.e. parallel motion estimation, for handling load variation in typical time-sharing computers. Therefore, the parallel workload can be distributed properly among n processors. In this paper, we present the development of resource monitoring for cluster computing using the MPI programming model and its application to parallel motion estimation. Results show the effectiveness of our method in which a faster parallel execution time can be achieved.
Circuits and Systems, 2002. APCCAS '02. 2002 Asia-Pacific Conference on (Volume:1 )
Date of Conference: 2002