A Unified Framework for Quantitative Cache Analysis

In this work we unify two existing lines of work towards cache analysis for non-LRU policies. To this end, we extend the notion of competitiveness to block competitiveness and systematically analyze the competitiveness and block competitiveness of FIFO and MRU relative to LRU for arbitrary associativities. We show how competitiveness and block competitiveness can be exploited in state-of-the-art WCET analysis based on the results of existing persistence analyses for LRU. Unlike prior work, our approach is applicable to microarchitectures that exhibit timing anomalies. We experimentally evaluate the precision and cost of our approach on benchmarks from TACLeBench. The experiments demonstrate that quantitative cache analysis for FIFO and MRU comes close to the precision of LRU.

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Publication Details

Published
2025-05-06
DOI
https://doi.org/10.1109/rtas65571.2025.00018
Citations
1
Primary Topic
Caching and Content Delivery
Type
article
Field-Weighted Citation Impact
1.49

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article

A Unified Framework for Quantitative Cache Analysis

Jan Reineke, Sophie Kahlen
1 citations
Caching and Content Delivery
1.49
article

A Unified Framework for Quantitative Cache Analysis

Jan Reineke, Sophie Kahlen
article en
1 citations

Abstract

In this work we unify two existing lines of work towards cache analysis for non-LRU policies. To this end, we extend the notion of competitiveness to block competitiveness and systematically analyze the competitiveness and block competitiveness of FIFO and MRU relative to LRU for arbitrary associativities. We show how competitiveness and block competitiveness can be exploited in state-of-the-art WCET analysis based on the results of existing persistence analyses for LRU. Unlike prior work, our approach is applicable to microarchitectures that exhibit timing anomalies. We experimentally evaluate the precision and cost of our approach on benchmarks from TACLeBench. The experiments demonstrate that quantitative cache analysis for FIFO and MRU comes close to the precision of LRU.

Saarland University (DE)
H2020 European Research Council
Openalex Percentile: Top 16%
Caching and Content Delivery
1.49
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