By Topic

Partitioning and scheduling for parallel image processing operations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cheolwhan Lee ; Dept. of Comput. Sci., California Univ., Santa Barbara, CA, USA ; Tao Yang ; Yuan-Fang Wang

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

Published in:

Parallel and Distributed Processing, 1995. Proceedings. Seventh IEEE Symposium on

Date of Conference:

25-28 Oct 1995