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One-Step Algorithm for Mixed Data and Task Parallel Scheduling Without Data Replication

Vincent Boudet 1, 2 Frédéric Desprez 1, 2 Frédéric Suter 1, 2
1 REMAP - Regularity and massive parallel computing
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : In this paper we propose an original algorithm for mixed data and task parallel scheduling. The main specificities of this algorithm are to simultane- ously perform the allocation and scheduling processes, and avoid the data replication. The idea is to base the scheduling on an accurate evaluation of each task of the application depending on the processor grid. Then no assumption is made with regard to the homogeneity of the execution platform. The complexity of our algorithm are given. Performance achieved by our schedules both in homogeneous and heterogeneous worlds, are compared to data-parallel executions for two applications: the complex matrix multiplicati- on and the Strassen decomposition.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 7:29:40 PM
Last modification on : Monday, November 23, 2020 - 9:56:08 AM


  • HAL Id : inria-00071994, version 1



Vincent Boudet, Frédéric Desprez, Frédéric Suter. One-Step Algorithm for Mixed Data and Task Parallel Scheduling Without Data Replication. [Research Report] RR-4591, LIP RR-2002-34, INRIA, LIP. 2002. ⟨inria-00071994⟩



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