Evolutionary Techniques for Parametric WCET Analysis

Estimating the worst-case execution time (WCET) of real-time programs is pivotal in their verification. WCET estimation either yields a numeric value that represents the maximum execution time of the program when executed on a specific hardware platform; or yields a parametric expression in the form of some function of the input which when instantiated with a particular input value, gives a WCET estimation of the program when triggered by this input specifically (on a specific hardware platform). Parametric WCET analysis provides extra accuracy as the WCET estimation can be tuned to particular input values at runtime; and is usually of interest to dynamic-scheduling schemes. In this paper we use genetic programming as an alternative method to approach the problem of parametric WCET analysis. Parametric expressions are captured automatically by the genetic program based on end-to-end executions of the program under analysis. The technique is complementary to static parametric WCET analysis and more amenable to industrial practice. Experimental evaluation shows that the presented technique computes accurate parametric expression in an almost negligible time

Parametric Timing Estimation With Newton-Gregory Formulae

To determine safe and tight worst-case execution time (WCET) estimates of scientific and multimedia codes that spent most of the execution time on executing loop iterations, efficient and accurate loop iteration count estimation methods are required. To support dynamic scheduling decisions based on WCET estimations, an effective loop iteration count estimation method should generate parametric formulae that can be evaluated at runtime. Therefore, the loop iteration count estimation methods utilized for WCET estimation must be effective in analyzing loops with symbolic bounds, non-rectangular loops, zero-trip loops, loops with multiple critical paths, and loops with non-unit strides. In this paper we present a novel approach to parametric WCET estimation to handle loops with both affine and nonaffine loop bounds in an efficent manner using a formulation based on Newton-Gregory interpolating polynomials