AGULLO, Emmanuel; AUGONNET, Cédric; DONGARRA, Jack; LTAIEF, Hatem; NAMYST, Raymond; ROMAN, Jean; THIBAULT, Samuel; TOMOV, Stanimire

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AGULLO, Emmanuel
High-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

AUGONNET, Cédric
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]

DONGARRA, Jack
Department of Computer Science. University of Tennessee
Oak Ridge National Laboratory [Oak Ridge] [ORNL]
School of Computer Science [Manchester]

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Symposium on Application Accelerators in High Performance Computing (SAAHPC), 2010-07-13, Knoxville. 2010-07

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Although the hardware has dramatically changed in the last few years, nodes of multicore chips augmented by Graphics Processing Units (GPUs) seem to be a trend of major importance. Previous approaches for scheduling dense linear operations on such a complex node led to high performance but at the double cost of not using the potential of all the cores and producing a static and non generic code. In this extended abstract, we present a new approach for scheduling dense linear algebra operations on multicore architectures with GPU accelerators using a dynamic scheduler capable of using the full potential of the node [1]. We underline the benefits both in terms of programmability and performance. We illustrate our approach with a Cholesky factorization relying on cutting edge GPU and CPU kernels [2], [3] achieving roughly 900 Gflop/s on an eight cores node accelerated with three NVIDIA Tesla GPUs.< Réduire

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Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators.

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