



@conference {2469,
	title = {Scheduling parallel tasks on heterogeneous clusters},
	booktitle = {Proceedings of the IASTED International Conference on Parallel and Distributed Computing and SystemsProc. IASTED INt. Conf. Parall. Distrib. Comput. Syst.},
	series = {Proceedings of the 16th IASTED International Conference on Parallel and Distributed Computing and Systems},
	volume = {16},
	year = {2004},
	note = {Conference code: 64182Export Date: 21 November 2012Source: ScopusArt. No.: 439-085Language of Original Document: EnglishCorrespondence Address: Barbosa, J.; Faculdade de Engenharia, Universidade do Porto, Inst. de Engenharia Biom{\'e}dica, Porto, Portugal; email: jbarbosa@fe.up.ptReferences: Amoura, A.K., Bampis, E., K{\"o}nig, J.-C., Scheduling algorithms for parallel gaussian elimination with communication costs (1998) IEEE Transactions on Parallel and Distributed Systems, 9 (7), pp. 679-686. , July;Barbosa, J., Padilha, A.J., Algorithm-dependent method to determine the optimal number of computers in parallel virtual machines (1998) VECPAR{\textquoteright}98, 1573. , Springer-Verlag LNCS; 
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Sponsors: Int. Association of Sci. and Technol. for Dev., IASTED; World Modelling and Simulation Forum (WMSF)},
	month = {2004///},
	pages = {449 - 454},
	address = {Cambridge, MA},
	abstract = {Parallel tasks, also called malleable tasks, are tasks that can be executed on any number of processors with its execution time being a function of the number of processors alloted to it. The scheduling of independent parallel tasks on homogeneous machines is a problem that has been extensively studied. For heterogeneous machines, with a diverse CPU capacity, new challenges arise, namely the selection of the set of CPUs that optimizes the processing time of a given task. This paper presents a methodology to determine the best allotment for each task that minimizes its processing time on a given heterogeneous machine. The aim is to improve the global processing time of a complex algorithm composed by several linear algebra kernels, by scheduling a number of parallel tasks in the heterogeneous machine. The results presented compare this approach to the simpler data parallel execution of each task on the heterogeneous cluster, i.e. executing one task at a time using the data parallel programming model.},
	keywords = {Computational complexity, Computer programming, CPU capacity, Data handling, Directed acyclic graphs (DAG), Graph theory, Heterogeneous clusters, Linear algebra, Parallel algorithms, Parallel processing systems, Parallel/malleable tasks, Program processors, Scheduling},
	isbn = {10272658 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-11844282098\&partnerID=40\&md5=c196fdfb0cc8b4c8a37688dd289071ca},
	author = {Barbosa, J. and Morais, C. and Monteiro, A.P.},
	editor = {Gonzalez T.}
}
