Using Dataflow Optimization Techniques with a Monadic Intermediate Language

TL;DR: This dissertation proves the efficacy of the dataflow algorithm's application to functional languages by giving a novel description of the uncurrying optimization in terms of theDataflow algorithm, as well as a complete implementation of the optimization using hoopl.

Abstract: Our work applies the dataflow algorithm to an area outside its traditional scope: functional languages. Our approach relies on a monadic intermediate language that provides low-level, imperative features like computed jumps and explicit allocations, while at the same time supporting high-level, functional-language features like case discrimination and partial application. We prototyped our work in Haskell using the hoopl library and this dissertation shows numerous examples demonstrating its use. We prove the efficacy of our approach by giving a novel description of the uncurrying optimization in terms of the dataflow algorithm, as well as a complete implementation of the optimization using hoopl.