Aggressive Aggregation: a New Paradigm for Program Optimization

Abstract

In this paper, we propose a new paradigm for program optimization which is based on aggressive aggregation, i.e., on a partial evaluation-based decomposition of acyclic program fragments into a pair of computationally optimal structures: an Algebraic Decision Diagram (ADD) to capture conditional branching and a parallel assignment that refers to an Expression DAG (ED) which realizes redundancy-free computation. The point of this decomposition into, in fact, side-effect-free component structures allows for powerful optimization that semantically comprise effects traditionally aimed at by SSA form transformation, code specialization, common subexpression elimination, and (partial) redundancy elimination. We illustrate our approach along an optimization of the well-known iterative Fibonacci program, which, typically, is considered to lack any optimization potential. The point here is that our technique supports loop unrolling as a first class optimization technique and is tailored to optimally aggregate large program fragments, especially those resulting from multiple loop unrollings. For the Fibonacci program, this results in a performance improvement beyond an order of magnitude.