Report Number: CSL-TR-92-518
Institution: Stanford University, Computer Systems Laboratory
Title: Avoiding Redundant Specialization during Partial Evaluation
Author: Ruf, Erik
Author: Weise, Daniel
Date: April 1992
Abstract: Existing partial evaluators use a strategy called polyvariant specialization, which involves specializing program points on the known portions of their arguments, and re-using such specializations only when these known portions match exactly. We show that this re-use criterion is overly restrictive, and misses opportunities for sharing in residual programs, thus producing large residual programs containing redundant specializations. We develop a criterion for re-use based on computing the domains of specializations, describe an approximate implementation of this criterion based on types, and show its implementation in our partial evaluation system FUSE. In addition, we describe several extensions to our mechanism to make it compatible with more powerful specialization strategies and to increase its efficiency. After evaluating our algorithm's usefulness, we relate it to existing work in partial evaluation and machine learning.