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Apprehending heterogeneity at (very) large scale

Abstract : The demand for computation power is steadily increasing, driven by the need tosimulate more and more complex phenomena with an increasing amount ofconsumed/produced data.To meet this demand, the High Performance Computing platforms grow in both sizeand heterogeneity.Indeed, heterogeneity allows splitting problems for a more efficient resolutionof sub-problems with ad hoc hardware or algorithms.This heterogeneity arises in the platforms' architecture and in the variety ofprocessed applications.Consequently, the performances become more sensitive to the execution context.We study in this thesis how to qualitatively bring—at a reasonablecost—context-awareness/obliviousness into allocation and scheduling policies.This study is conducted from two standpoints: within single applications, andat the whole platform scale from an inter-applications perspective.We first study the minimization of the makespan of sequential tasks onplatforms with a mixed architecture composed of multiple CPUs and GPUs.We integrate context-awareness into schedulers with an affinity mechanism thatimproves local behavior.This mechanism has been implemented in a parallel run-time, and experimentsshow that it is able to reduce the memory transfers while maintaining a lowmakespan.We then extend the model to implicitly consider parallelism on the CPUs withthe moldable-task model.We propose an efficient algorithm formulated as an integer linear program witha constant performance guarantee of 3/2+ε.Second, we devise a new modeling framework where constraints are a first-classtool.Rather than extending existing models to consider all possible interactions, wereduce the set of feasible schedules by further constraining existing models.We propose a set of reasonable constraints to model application spreading andI/O traffic.We then instantiate this framework for unidimensional topologies, and propose acomprehensive case study of the makespan minimization under convex and localconstraints.
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Submitted on : Wednesday, May 23, 2018 - 11:49:06 AM
Last modification on : Thursday, October 21, 2021 - 3:53:36 AM
Long-term archiving on: : Friday, August 24, 2018 - 8:25:01 PM


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  • HAL Id : tel-01722991, version 2




Raphaël Bleuse. Apprehending heterogeneity at (very) large scale. Modeling and Simulation. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAM053⟩. ⟨tel-01722991v2⟩



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