Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

Xavier Lacoste, Mathieu Faverge, Pierre Ramet, Samuel Thibault, George Bosilca
Institut Polytechnique de Bordeaux, HiePACS (INRIA Bordeaux – Sud-Ouest), INRIA – PRES Universite de Bordeaux – CNRS : UMR5800 – CERFACS
hal-00925017, (23 January 2014)




   title={Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes},

   author={Lacoste, Xavier and Faverge, Mathieu and Ramet, Pierre and Thibault, Samuel and Bosilca, George},

   keywords={Sparse linear solver; DAG based runtime; multicore; GPU},


   affiliation={HiePACS – INRIA Bordeaux – Sud-Ouest, Laboratoire Bordelais de Recherche en Informatique – LaBRI, RUNTIME – INRIA Bordeaux – Sud-Ouest, Innovative Computing Laboratory – ICL},


   type={Rapport de recherche},







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The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of the computing resources. The pressure to maintain reasonable levels of performance and portability, forces the application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical architectures. In this paper, we study the replacement of the highly specialized internal scheduler in PaStiX by two generic runtime frameworks: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them with the opportunity to optimize it in order to maximize the algorithm efficiency for a predefined execution environment. A comparative study of the performance of the PaStiX solver with the three schedulers – native PaStiX, StarPU and PaRSEC schedulers – on different execution contexts is performed. The analysis highlights the similarities from a performance point of view between the different execution supports. These results demonstrate that these generic DAG-based runtimes provide a uniform and portable programming interface across heterogeneous environments, and are, therefore, a sustainable solution for hybrid environments.
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