11845

Modeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-Core Architectures

Luka Stanisic, Samuel Thibault, Arnaud Legrand, Brice Videau, Jean-Francois Mehaut
CNRS: UMR5217 – INRIA – Universite Joseph Fourier – Grenoble I – Institut polytechnique de Grenoble (Grenoble INP) – Laboratoire d’Informatique de Grenoble : LIG
hal-00966862, (28 March 2014)

@techreport{stanisic:hal-00966862,

   hal_id={hal-00966862},

   url={http://hal.inria.fr/hal-00966862},

   title={Modeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-Core Architectures},

   author={Stanisic, Luka and Thibault, Samuel and Legrand, Arnaud and Videau, Brice and M{‘e}haut, Jean-Fran{c c}ois},

   keywords={Simulation; Runtime; Hybrid platforms},

   language={Anglais},

   affiliation={MESCAL – INRIA Grenoble Rh{^o}ne-Alpes / LIG laboratoire d’Informatique de Grenoble , Laboratoire d’Informatique de Grenoble – LIG , Laboratoire Bordelais de Recherche en Informatique – LaBRI , RUNTIME – INRIA Bordeaux – Sud-Ouest},

   type={Rapport de recherche},

   institution={INRIA},

   number={RR-8509},

   year={2014},

   month={Mar},

   pdf={http://hal.inria.fr/hal-00966862/PDF/RR-8509.pdf}

}

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Multi-core architectures comprising several GPUs have become mainstream in the field of High-Performance Computing. However, obtaining the maximum performance of such heterogeneous machines is challenging as it requires to carefully offload computations and manage data movements between the different processing units. The most promising and successful approaches so far rely on task-based runtimes that abstract the machine and rely on opportunistic scheduling algorithms. As a consequence, the problem gets shifted to choosing the task granularity, task graph structure, and optimizing the scheduling strategies. Trying different combinations of these different alternatives is also itself a challenge. Indeed, getting accurate measurements requires reserving the target system for the whole duration of experiments. Furthermore, observations are limited to the few available systems at hand and may be difficult to generalize. In this research report, we show how we crafted a coarse-grain hybrid simulation/emulation of StarPU, a dynamic runtime for hybrid architectures, over SimGrid, a versatile simulator for distributed systems. This approach allows to obtain performance predictions accurate within a few percents on classical dense linear algebra kernels in a matter of seconds, which allows both runtime and application designers to quickly decide which optimization to enable or whether it is worth investing in higher-end GPUs or not.
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