Optimizing Program Size Using Multi-result Supercompilation

TL;DR

This paper introduces a new approach to supercompilation that controls program size by combining multi-result supercompilation with a generalization strategy, leading to smaller, optimized programs.

Contribution

It presents a novel method for size control in supercompilation using multi-result techniques and generalization, improving predictability and efficiency.

Findings

Results show smaller program sizes after transformation.

The method maintains powerful optimization capabilities.

Early experiments indicate promising potential.

Abstract

Supercompilation is a powerful program transformation technique with numerous interesting applications. Existing methods of supercompilation, however, are often very unpredictable with respect to the size of the resulting programs. We consider an approach for controlling result size, based on a combination of multi-result supercompilation and a specific generalization strategy, which avoids code duplication. The current early experiments with this method show promising results - we can keep the size of the result small, while still performing powerful optimizations.