Linear induction variable detection is usually associated with the strength reduction optimization. For restructuring compilers, effective data dependence analysis requires that the compiler detect and accurately describe linear and nonlinear induction variables as well as more general sequences. In this article we present a practical technique for detecting a broader class of linear induction variables than is usually recognized, as well as several other sequence forms, including periodic, polynomial, geometric, monotonic, and wrap-around variables. Our method is based on Factored Use-Def (FUD) chains, a demand-driven representation of the popular Static Single Assignment (SSA) form. In this form, strongly connected components of the associated SSA graph correspond to sequences in the source program: we describe a simple yet efficient algorithm for detecting and classifying these sequences. We have implemented this algorithm in Nascent, our restructuring Fortran 90+ compiler, and we present some results showing the effectiveness of our approach.

No abstract

Constant propagation is a well-known static compiler technique in which values of variables which are determined to be constants can be passed to expressions which use these constants. Code size reduction, bounds propagation, and dead-code elimination are some of the optimizations which benefit from this analysis.In this'paper, we present a new method for detecting constants, based upon an optimistic demand-driven recursive solver, as opposed to more traditional iterative solvers. The problem with iterative solvers is that they may evaluate an expression many times, while our technique evaluates each expression only once. To consider conditional code, wc augment the standard Static Single Assignment (SSA) form with merge operators called v-functions, adapted from the interpretable Gated Single Assignment (GSA) model.

No abstract