Determining average program execution times and their variance
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
Static performance prediction of data-dependent programs
Proceedings of the 2nd international workshop on Software and performance
On Predicting Data Cache Behavior for Real-Time Systems
LCTES '98 Proceedings of the ACM SIGPLAN Workshop on Languages, Compilers, and Tools for Embedded Systems
Cache Behavior Prediction by Abstract Interpretation
SAS '96 Proceedings of the Third International Symposium on Static Analysis
Timing Analysis for Data Caches and Set-Associative Caches
RTAS '97 Proceedings of the 3rd IEEE Real-Time Technology and Applications Symposium (RTAS '97)
Stochastic Analysis of Periodic Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Static Determination of Probabilistic Execution Times
ECRTS '04 Proceedings of the 16th Euromicro Conference on Real-Time Systems
Schedulability analysis of applications with stochastic task execution times
ACM Transactions on Embedded Computing Systems (TECS)
Bounding Worst-Case Data Cache Behavior by Analytically Deriving Cache Reference Patterns
RTAS '05 Proceedings of the 11th IEEE Real Time on Embedded Technology and Applications Symposium
WCET estimation for executables in the presence of data caches
EMSOFT '07 Proceedings of the 7th ACM & IEEE international conference on Embedded software
Executable Analysis using Abstract Interpretation with Circular Linear Progressions
MEMOCODE '07 Proceedings of the 5th IEEE/ACM International Conference on Formal Methods and Models for Codesign
The worst-case execution-time problem—overview of methods and survey of tools
ACM Transactions on Embedded Computing Systems (TECS)
Cache modeling in probabilistic execution time analysis
Proceedings of the 45th annual Design Automation Conference
Communications of the ACM - Security in the Browser
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In this paper, we propose a formal analysis approach to estimate the expected (average) data cache access time of an application across all possible program inputs. Towards this goal, we introduce the notion of probabilistic access history that intuitively summarizes the history of data memory accesses along different program paths (to reach a particular program point) and their associated probabilities. An efficient static program analysis technique has been developed to compute the access history at all program points. We estimate the cache hit/miss probabilities and hence the expected access time of each data memory reference from the access history. Our experimental evaluation confirms the accuracy and viability of the probabilistic data cache modeling approach.