Polynomial time scheduling of low level computer vision algorithms on networks of heterogeneous machines

Defining an optimal schedule for arbitrary algorithms on a network of heterogeneous machnes is an NP complete problem. By focusing on data parallel deterministic neighborhood computer vision algorithms, a minimum time schedule can be defined in polynomial time. The scheduling model allows for any speed machine to participate in the concurrent computation but makes the assumption of a masterklave control mechanism using a linear communication network. Several vision algorithms are presented which adhere to the scheduling model. The theoretical speedup of these algorithms is discussed and empirical data is presented and compared to theoretical results.

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Scheduling of low level computer vision algorithms on networks of heterogeneous machines

Nolan

Everding

Wee

Proceedings of Conference on Computer Architectures for Machine Perception

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Polynomial time scheduling of low level computer vision algorithms on networks of heterogeneous machines

Cited by 1 publication

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Scheduling of low level computer vision algorithms on networks of heterogeneous machines

Scheduling of low level computer vision algorithms on networks of heterogeneous machines

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