Abstract de la publi numéro 5193
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 use
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.