François Irigoin

Minimal data dependence abstractions for loop transformations: extended version

Many abstractions of program dependences have already been proposed, such as the Dependence Distance, the Dependence Direction Vector, the Dependence Level or the Dependence Cone. These different abstractions have different precisions. Theminimal abstraction associated to a transformation is the abstraction that contains the minimal amount of information necessary to decide when such a transformation is legal. Minimal abstractions for loop reordering and unimodular transformations are presented. As an example, the dependence cone, which approximates dependences by a convex cone of the dependence distance vectors, is the minimal abstraction for unimodular transformations. It also contains enough information for legally applying all loop reordering transformations and finding the same set of valid mono- and multi-dimensional linear schedules as the dependence distance set.