Reference : An improved genetic algorithm for efficient scheduling on distributed memory parallel...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/15402

Title :	An improved genetic algorithm for efficient scheduling on distributed memory parallel systems
Language :	English
Author, co-author :	Pecero, Johnatan [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	19-May-2010
Main document title :	ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010
Publisher :	IEEE
Pages :	1 - 8
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-4244-7716-6
Event name :	International Conference on Computer Systems and Applications
Event date :	from 16-05-2010 to 19-05-2010
Event country :	Tunisia
Keywords :	[en] Parallel and Distributed Computing ; Performance of Systems ; Scheduling ; Genetic Algorithms ; Optimization ; Task Clustering
Abstract :	[en] A key issue related to the distributed memory multiprocessors architecture for achieving high performance computing is the efficient scheduling of heavily communicated parallel applications such that the total execution time is minimized. Therefore, this paper provides a genetic algorithm based on task clustering techniques for scheduling parallel applications with large communication delays on distributed memory parallel systems. The genetic algorithm is improved with the introduction of some extra knowledge about the scheduling problem. This knowledge is represented by a class of clustering heuristic which is based on structural properties of the parallel application. The major feature of the proposed algorithm is that it takes advantage of the effectiveness of task clustering for reducing communication delays combined with the ability of the genetic algorithms for exploring and exploiting information of the search space of the scheduling problem. The algorithm is assessed by simulation run on some families of traced graphs which represents some of the numerical parallel application programs, and a set of randomly generated applications. Simulation results showed that this algorithm significantly improves the performance of related approaches.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/15402
DOI :	10.1109/AICCSA.2010.5587030
Commentary :	Computer Systems and Applications (AICCSA), 2010 IEEE/ACS International Conference on

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