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On the specialization of online program specializers1

Part of: JFP Research Articles

Published online by Cambridge University Press:  07 November 2008

Erik Ruf  and
Daniel Weise
Erik Ruf
Affiliation:
Stanford University
Daniel Weise
Affiliation:
Microsoft Research Laboratory
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Abstract

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A common technique for improving the speed of program specialization is to specialize the program specializer itself on the program to be specialized, creating a custom program generator. Much research has been devoted to the problem of generating efficient program generators, which do not perform reductions at program generation time which could instead have been performed when the program generator was constructed. The conventional wisdom holds that only program specializers using binding-time approximations can be specialized into such efficient program generators. This paper argues that this is not the case, and demonstrates that the specialization of a nontrivial online program specializer similar to the original ‘naive MIX’ can indeed yield an efficient program generator. The key to our argument is that, while the use of binding-time information at program generator generation time is necessary for the construction of an efficient custom specializer, the use of explicit binding-time approximation techniques is not. This allows us to distinguish the problem at hand (i.e. the use of binding-time information during program generator generation) from particular solutions to that problem (i.e. offline specialization). We show that, given a careful choice of specializer data structures, and sufficiently powerful specialization techniques, binding-time information can be inferred and utilized without explicit approximations. This allows the construction of efficient, optimizing program generators from online program specializers.

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Type
Articles
Information
Journal of Functional Programming , Volume 3 , Issue 3 , July 1993 , pp. 251 - 281
Copyright
Copyright © Cambridge University Press 1993

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