Acceleration in Convex Data-Flow Analysis

Jérôme Leroux (1) , Grégoire Sutre (1)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique

In abstract interpretation-based data-flow analysis, widening operators are usually used in order to speed up the iterative computation of the minimum fix-point solution (MFP). However, the use of widenings may lead to loss of precision in the analysis. Acceleration is an alternative to widening that has mainly been developed for symbolic verification of infinite-state systems. Intuitively, acceleration consists in computing the exact effect of some control-flow cycle in order to speed up reachability analysis. This paper investigates acceleration in convex data-flow analysis of systems with real-valued variables where guards are convex polyhedra and assignments are translations. In particular, we present a simple and algorithmically efficient characterization of MFP-acceleration for cycles with a unique initial location. We also show that the MFP-solution is a computable algebraic polyhedron for systems with two variables.

Domaines: Algorithme et structure de données [cs.DS]

Jérôme Leroux, Grégoire Sutre. Acceleration in Convex Data-Flow Analysis. FSTTCS, 2007, New Delhi, India. pp.520-531, ⟨10.1007/978-3-540-77050-3_43⟩. ⟨hal-00346295⟩