Efficient and Precise Cache Behavior Prediction for Real-Time Systems

Abstract interpretation is a technique for the static detection of dynamic properties of programs. It is semantics based, that is, it computes approximative properties of the semantics of programs. On this basis, it supports correctness proofs of analyses. It replaces commonly used ad hoc techniques by systematic, provable ones, and it allows for the automatic generation of analyzers from specifications by existing tools. In this work, abstract interpretation is applied to the problem of predicting the cache behavior of programs. Abstract semantics of machine programs are defined which determine the contents of caches. For interprocedural analysis, existing methods are examined and a new approach that is especially tailored for the cache analysis is presented. This allows for a static classification of the cache behavior of memory references of programs. The calculated information can be used to improve worst case execution time estimations. It is possible to analyze instruction, data, and combined instruction/data caches for common (re)placement and write strategies. Experimental results are presented that demonstrate the applicability of the analyses.