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Communication Dans Un Congrès Année : 2021

Experimental Evaluation of Statistical Model Checking Methods for Probabilistic Timing Analysis of Multiprocessor Systems

Résumé

Timing prediction of complex parallel data flow applications on multiprocessor systems represents a difficult task due to complex interferences observed by running software on platforms shared resources. In this domain, classical analytical or simulation-based approaches demonstrate scalability issues to deliver fast yet accurate predictions. In this work, we present an experimental evaluation of new simulation-based statistical methods for timing analysis of multiprocessor systems. We adopt a measurement-based approach for the creation of probabilistic system-level models of the studied systems. Efficiency of statistical methods is evaluated for platforms with different levels of complexity from the point of view of shared resources. We compare our approach against measurement and traditional simulation methods on two case-studies from the computer vision domain: a Sobel filter and a JPEG decoder. We show that the accuracy and execution time of our simulation approach has good potential for fast yet accurate design space exploration.
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Dates et versions

hal-03263210 , version 1 (17-06-2021)

Identifiants

  • HAL Id : hal-03263210 , version 1

Citer

Hai-Dang Vu, Sébastien Le Nours, Sébastien Pillement. Experimental Evaluation of Statistical Model Checking Methods for Probabilistic Timing Analysis of Multiprocessor Systems. Euromicro DSD 2021, Sep 2021, Palerme, Italy. paper 37. ⟨hal-03263210⟩
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