Cited By
View all- Touzeau VReineke J(2023)Leveraging LLVM's ScalarEvolution for Symbolic Data Cache Analysis2023 IEEE Real-Time Systems Symposium (RTSS)10.1109/RTSS59052.2023.00029(237-250)Online publication date: 5-Dec-2023
Data Cache Analysis by Counting Integer Points
Pages 112 - 122
Abstract
The scheduling of reliable real-time systems require a precise and sound analysis of the execution times of their tasks. Part of these execution times is spent fetching data from the main memory to the cache memories. These fetch events occur on cache misses, but cache misses are hard to predict when the program accesses an array. An imprecise cache miss analysis can lead to an imprecise but still sound Worst-Case Execution Time (WCET) analysis. In this article we present a framework for deriving an upper bound to the number of times a data-accessing instruction triggers a cache miss. Backed by this framework, we present an analysis that produces numeric or symbolic bounds, by reasoning on a short history of accesses and by counting the number of integer points in a volume with an existing tool (Barvinok). Using such bounds will improve the precision of the estimations delivered by the WCET analyses.
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April 2021
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Published: 22 July 2021
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RTNS'2021
RTNS'2021: 29th International Conference on Real-Time Networks and Systems
April 7 - 9, 2021
NANTES, France
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View all- Touzeau VReineke J(2023)Leveraging LLVM's ScalarEvolution for Symbolic Data Cache Analysis2023 IEEE Real-Time Systems Symposium (RTSS)10.1109/RTSS59052.2023.00029(237-250)Online publication date: 5-Dec-2023
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