Partitioning and scheduling for parallel image processing operations

Many computer vision and image processing (CVIP) operations can be represented as a sequence of tasks with nested loops, specified by the visual programming language Khoros. This paper addresses the automatic partitioning and scheduling of such operations on distributed memory multiprocessors. The m...

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Bibliographic Details
Published inProceedings.Seventh IEEE Symposium on Parallel and Distributed Processing pp. 86 - 90
Main Authors Cheolwhan Lee, Tao Yang, Yuan-Fang Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 1995
Subjects
Computer science
Computer vision
Concurrent computing
Cost function
Distributed computing
Dynamic scheduling
Image processing
Partitioning algorithms
Processor scheduling
Runtime
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Summary:Many computer vision and image processing (CVIP) operations can be represented as a sequence of tasks with nested loops, specified by the visual programming language Khoros. This paper addresses the automatic partitioning and scheduling of such operations on distributed memory multiprocessors. The major difficulties in determining the optimal image data distribution for each task are that the number of processors available and the size of the input image may vary at the run time, and the cost of some image processing operations may be data-dependent. This paper proposes a compile-time processor assignment and data partitioning scheme that optimizes the average run-time performance of task chains with nested loops by considering the data redistribution overheads and possible run-time parameter variations. This paper presents the theoretical analysis and experimental results on a Meiko CS-2 distributed memory machine.
ISBN:9780818671951
0818671955
ISSN:1063-6374
DOI:10.1109/SPDP.1995.530669