BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CSL-TR-92-518
       ENTRY:: November 02, 1994
ORGANIZATION:: Stanford University, Computer Systems Laboratory
       TITLE:: Avoiding Redundant Specialization during Partial Evaluation
        TYPE:: Technical Report
      AUTHOR:: Ruf, Erik
      AUTHOR:: Weise, Daniel
        DATE:: April 1992
       PAGES:: 48
    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.
       NOTES:: [Adminitrivia V1/Prg/19941102]
         END:: STAN//CSL-TR-92-518