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Proceedings Paper

Parallel processing of multi-dimensional data with causal neighborhood dependencies
Author(s): Deepak S. Turaga; Krishna Ratakonda
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Paper Abstract

In this paper, we investigate the problem of enabling block level parallelism, for multi-dimensional data sets, with arbitrary but static causal dependency between blocks that constitute the data set. As the use of video and other multi-dimensional data sets becomes more common place and the algorithms used to process them become more complex, there is greater need for effective parallelization schemes. We describe a method for synchronizing the execution of multiple processors to respect the dependency structure and calculate the total processing time as a function of the number of parallel processors. We also provide an algorithm to calculate the optimal starting times for each processor which enables them to continuously process blocks without the need for synchronizing with other processors, under the assumption that the time to process each block is fixed.

Paper Details

Date Published: 28 January 2008
PDF: 7 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 682219 (28 January 2008); doi: 10.1117/12.766977
Show Author Affiliations
Deepak S. Turaga, IBM T.J. Watson Research Ctr. (United States)
Krishna Ratakonda, IBM T.J. Watson Research Ctr. (United States)

Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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