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Section: Research Program

Worst case execution time estimation of a program

Modern processors induce an increased variability of the execution time of programs, making difficult (or even impossible) a complete static analysis. Our objective is to propose a solution composing probabilistic and non-probabilistic approaches based both on static and on statistical analyses by answering the following scientific challenges:

  1. a classification of the variability of execution times of a program with respect to the processor features. We will use as first measure our statistical estimator based on the Extreme Value Theory [18], [20]. An implementation of the estimator is available at The access to this later page requires a login (aoste) and a password (aoste). The difficulty of this challenge is related to the definition of an element belonging to the set of variability factors and its mapping to the execution time of the program.

  2. a compositional rule of statistical models based on Bayesian approaches. The difficulty of this challenge comes from the fact that a global maximum cannot be obtained by upper bounding the corresponding local maxima. We will use as first rule of composition a Bayesian approach [22]. We consider as first statistical model those obtained by any static analysis of the program on a basic processor. Through the Bayesian approach we add iteratively the variability due to each processor feature as a new statistical model. The convergence of the global model is decided once no variability is detected at the level of the statistical estimator providing the bounds on the execution time of the program.

The problem of estimating the worst case execution time of a program is an excellent opportunity for the Extreme Values community to validate and to evolve as the context of obtaining measures is indefinitely reproducible.