Generalized Boxings, Congruences and Partial Inlining

We present a new technique for optimizing programs, based on data-ow analysis. The initial goal was to provide a simple way to improve on Leroy and Peyton-Jones' techniques for optimizing boxings (allocations) and unboxings ((eld selection) away in strongly-typed functional programs. Our techniques achieve this goal, while not needing types any more (so it applies to Lisp as well as to ML), and providing a ner analysis; moreover, our techniques also improve on classical common subexpression elimination. The methods are twofold: intraprocedurally, we use a data-ow analysis technique that propagates congruences on program variables and terms built on these variables; interprocedurally, we help the intraprocedural analysis by inlining, at least partially, all functions whose deenitions are known at call sites.