Pref-X: a framework to reveal data prefetching in commercial in-order cores - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2022

Pref-X: a framework to reveal data prefetching in commercial in-order cores

Quentin Huppert
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  • PersonId : 1086569
  • IdRef : 268596239
Francky Catthoor
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  • PersonId : 1086572
Lionel Torres
David Novo

Abstract

Computer system simulators are major tools used by architecture researchers to develop and evaluate new ideas. Clearly, such evaluations are more conclusive when compared to commercial state-of-the-art architectures. However, the behavior of key components in existing processors is often not disclosed, complicating the construction of faithful reference models. The data prefetching engine is one of such obscured components that can have a significant impact on key metrics such as performance and energy. In this paper, we propose Pref-X, a framework to analyze functional characteristics of data prefetching in commercial in-order cores. Our framework reveals data prefetches by X-raying into the cache memory at the request granularity, which allows linking memory access patterns with changes in the cache content. To demonstrate the power and accuracy of our methodology, we use Pref-X to replicate the data prefetching mechanisms of two representative processors, namely the Arm Cortex-A7 and the Arm Cortex-A53, with a 99.8% and 96.9% average accuracy, respectively.
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Dates and versions

lirmm-03767077 , version 1 (01-09-2022)

Identifiers

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Quentin Huppert, Francky Catthoor, Lionel Torres, David Novo. Pref-X: a framework to reveal data prefetching in commercial in-order cores. DAC 2022 - 59th ACM/IEEE Design Automation Conference, Jul 2022, San Francisco, CA, United States. pp.1051-1056, ⟨10.1145/3489517.3530569⟩. ⟨lirmm-03767077⟩
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