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Flexible Representation Analysis

Last modified: Tue Mar 14 19:27:24 2000 GMT.

Authors

Zhong Shao

Abstract

Statically typed languages with Hindley-Milner polymorphism have long been compiled using inefficient and fully boxed data representations. Recently, several new compilation methods have been proposed to support more efficient and unboxed multi-word representations. Unfortunately, none of these techniques is fully satisfactory. For example, Leroy's coercion-based approach does not handle recursive data types and mutable types well. The type-passing approach (proposed by Harper and Morrisett) handles all data objects, but it involves extensive runtime type analysis and code manipulations.

This paper presents a new flexible representation analysis technique that combines the best of both approaches. Our new scheme supports unboxed representations for recursive and mutable types, yet it only requires little runtime type analysis. In fact, we show that there is a continuum of possibilities between the coercion-based approach and the type-passing approach. By varying the amount of boxing and the type information passed at runtime, a compiler can freely explore any point in the continuum--choosing from a wide range of representation strategies based on practical concerns. Finally, our new scheme also easily extends to handle type abstractions across ML-like higher-order modules.

Published

  • In Proc. 1997 ACM SIGPLAN International Conference on Functional Programming (ICFP'97), Amsterdam, The Netherlands, pages 85-98, June 1997. ©1997 ACM.
  • Technical Report YALEU/DCS/TR-1125, Department of Computer Science, Yale University, April 1997.

Copyright Notice

Copyright © 1997 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1(212)869-0481, or permissions@acm.org.

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