Monotonic Evolution: an Alternative to Induction Variable Substitution for Dependence Analysis

Peng Wu (1) , Albert Cohen (2, 1) , Jay Hoeflinger (3) , David Padua (4)

1 UIUC - Department of Computer Science [UIUC]
2 PRISM - Parallélisme, Réseaux, Systèmes, Modélisation
3 Intel Corporation [USA]
4 UIUC - University of Illinois at Urbana-Champaign [Urbana]

We present a new approach to dependence testing in the presence of induction variables. Instead of looking for closed form expressions, our method computes monotonic evolution which captures the direction in which the value of a variable changes. This information is then used in the dependence test to help determine whether array references are dependence-free. Under this scheme, closed form computation and induction variable substitution can be delayed until after the dependence test and be performed on-demand. To improve computational efficiency, we also propose an optimized (non-iterative) data-flow algorithm to compute evolution. Experimental results show that dependence tests based on evolution information matches the accuracy of that based on closed-form computation (implemented in Polaris), and when no closed form expressions can be calculated, our method is more accurate than that of Polaris.

Domaines: Langage de programmation [cs.PL]; Calcul parallèle, distribué et partagé [cs.DC]; Systèmes embarqués

Peng Wu, Albert Cohen, Jay Hoeflinger, David Padua. Monotonic Evolution: an Alternative to Induction Variable Substitution for Dependence Analysis. Intl. Conf. on Supercomputing, Jun 2001, Sorrento, Italy. ⟨hal-01257312⟩