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Modeling of applications and hardware to explore task mapping and scheduling strategies on a heterogeneous micro-server system

Abstract : Many of todays important applications of our everyday lives, e.g. weather forecast, design of plane and car shapes, medical analysis or even search engine queries depend on massively-parallel computer programs that are executed in data centers hosting thousands of computers. A large amount of electrical energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems. The rationale for that is that at least parts of applications execute more efficiently depending on the computing resource (processors, accelerators, etc.). Nevertheless, the exploitation of these heterogeneous platforms raises new challenges in terms of application management optimization on available computing resources. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding the best mapping and scheduling of an application to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such as model, provide two implementations with state-of-the-art tools and preliminary mapping and scheduling numerical results.
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https://hal-cea.archives-ouvertes.fr/cea-01838133
Contributor : Léna Le Roy <>
Submitted on : Friday, July 13, 2018 - 10:28:33 AM
Last modification on : Monday, February 10, 2020 - 6:14:16 PM

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L. Zaourar, M.A. Aba, D. Briand, J.-M. Philippe. Modeling of applications and hardware to explore task mapping and scheduling strategies on a heterogeneous micro-server system. 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), May 2017, Lake Buena Vista, United States. pp.65-76, ⟨10.1109/IPDPSW.2017.123⟩. ⟨cea-01838133⟩

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