The use of caches challenges measurement-based timing analysis (MBTA) in critical embedded systems. In the presence of caches, the worst-case timing behavior of a system heavily depends on how code and data are laid out in cache. Guaranteeing that test runs capture, and hence MBTA results are representative of, the worst-case conflictive cache layouts, is generally unaffordable for end users. The probabilistic variant of MBTA, MBPTA, exploits randomized caches and relieves the user from the burden of concocting layouts. In exchange, MBPTA requires the user to control the number of runs so that a solid probabilistic argument can be made about having captured the effect of worst-case cache conflicts during analysis. We present a computationally tractable Time-aware Address Conflict (TAC) mechanism that determines whether the impact of conflictive memory layouts is indeed captured in the MBPTA runs and prompts the user for more runs in case it is not.

Software time reliability in the presence of cache memories

Vardanega, Tullio
Supervision
;
CAZORLA ALMEIDA, FRANCISCO JAVIER
Supervision
2017

Abstract

The use of caches challenges measurement-based timing analysis (MBTA) in critical embedded systems. In the presence of caches, the worst-case timing behavior of a system heavily depends on how code and data are laid out in cache. Guaranteeing that test runs capture, and hence MBTA results are representative of, the worst-case conflictive cache layouts, is generally unaffordable for end users. The probabilistic variant of MBTA, MBPTA, exploits randomized caches and relieves the user from the burden of concocting layouts. In exchange, MBPTA requires the user to control the number of runs so that a solid probabilistic argument can be made about having captured the effect of worst-case cache conflicts during analysis. We present a computationally tractable Time-aware Address Conflict (TAC) mechanism that determines whether the impact of conflictive memory layouts is indeed captured in the MBPTA runs and prompts the user for more runs in case it is not.
2017
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9783319605876
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3257900
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