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Hybrid scheduling for the parallel solution of linear systems

Abstract : In this paper, we consider the problem of designing a dynamic scheduling strategy that takes into account both workload and memory information in the context of the parallel multifrontal factorization. The originality of our approach is that we base our estimations (work and memory) on a static optimistic scenario during the analysis phase. This scenario is then used during the factorization phase to constrain the dynamic decisions. The task scheduler has been redesigned to take into account these new features. Moreover performance have been improved because the new constraints allow the new scheduler to make optimal decisions that were forbidden or too dangerous in unconstrained formulations. Performance analysis show that the memory estimation becomes much closer to the memory effectively used and that even in a constrained memory environment we decrease the factorization time with respect to the initial approach.
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Submitted on : Friday, May 19, 2006 - 8:58:36 PM
Last modification on : Wednesday, October 26, 2022 - 8:15:53 AM


  • HAL Id : inria-00070599, version 1


Patrick Amestoy, Abdou Guermouche, Jean-Yves L'Excellent, Stéphane Pralet. Hybrid scheduling for the parallel solution of linear systems. [Research Report] RR-5404, LIP RR-2004-53, INRIA : Institut national de recherche en sciences et technologies du numérique; LIP : Laboratoire de l’Informatique du Parallélisme, ENS Lyon. 2004, pp.1-28. ⟨inria-00070599⟩



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