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Parallel Computing 32, 2 (2006) 136-156
Hybrid scheduling for the parallel solution of linear systems
Jean-Yves L'Excellent 1, Patrick R. Amestoy 2, Stéphane Pralet 2, Abdou Guermouche 3, 4
For the GRAAL INRIA Rhone-Alpes, ENSEEIHT-IRIT collaboration(s)
(2006)

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 that compute fully irregular partitions in order to better balance the workload. We show that our new scheduling algorithm significantly improves both the memory behaviour and the factorization time. We give experimental results for large challenging real-life 3D problems on 64 and 128 processors.
1:  GRAAL (INRIA Grenoble Rhône-Alpes / LIP Laboratoire de l'Informatique du Parallélisme)
CNRS : UMR5668 – INRIA – École Normale Supérieure - Lyon – Université Claude Bernard - Lyon I – Laboratoire d'informatique du Parallélisme
2:  Institut de recherche en informatique de Toulouse (IRIT)
CNRS : UMR5505 – Institut National Polytechnique de Toulouse - INPT – Université des Sciences Sociales - Toulouse I – Université Toulouse I [UT1] Capitole – Université Toulouse le Mirail - Toulouse II – Université Paul Sabatier [UPS] - Toulouse III
3:  Laboratoire Bordelais de Recherche en Informatique (LaBRI)
CNRS : UMR5800 – Université Sciences et Technologies - Bordeaux I – École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB) – Université Victor Segalen - Bordeaux II
4:  SCALAPPLIX (INRIA Futurs)
INRIA – CNRS : UMR5466 – Université Sciences et Technologies - Bordeaux I – Université Victor Segalen - Bordeaux II – École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)
Computer Science/Distributed, Parallel, and Cluster Computing