Tobias Grosser

Lambda the Ultimate SSA: Optimizing Functional Programs in SSA

Siddharth Bhat, Tobias Grosser, 2022

Paper Overview

Static Single Assignment (SSA) is the workhorse of modern optimizing compilers for imperative programming languages. However, functional languages have been slow to adopt SSA and prefer to use intermediate representations based on minimal lambda calculi due to SSA's inability to express higher order constructs. We exploit a new SSA construct -- regions -- in order to express functional optimizations via classical SSA based reasoning. Region optimization currently relies on ad-hoc analyses and transformations on imperative programs. These ad-hoc transformations are sufficient for imperative languages as regions are used in a limited fashion. In contrast, we use regions pervasively to model sub-expressions in our functional IR. This motivates us to systematize region optimizations. We extend classical SSA reasoning to regions for functional-style analyses and transformations. We implement a new SSA+regions based backend for LEAN4, a theorem prover that implements a purely functional, dependently typed programming language. Our backend is feature-complete and handles all constructs of LEAN4's functional intermediate representation \lambdarc within the SSA framework. We evaluate our proposed region optimizations by optimizing \lambdarc within an SSA+regions based framework implemented in MLIR and demonstrating performance parity with the current LEAN4 backend. We believe our work will pave the way for a unified optimization framework capable of representing, analyzing, and optimizing both functional and imperative languages.

Paper arXiv