Representation-based just-in-time specialization and the Psyco prototype for Python (2004)
| Venue: | Proceedings of the 2004 ACM SIGPLAN Workshop on Partial Evaluation and Semantics-based Program Manipulation |
| Citations: | 31 - 2 self |
BibTeX
@INPROCEEDINGS{Rigo04representation-basedjust-in-time,
author = {Armin Rigo},
title = {Representation-based just-in-time specialization and the Psyco prototype for Python},
booktitle = {Proceedings of the 2004 ACM SIGPLAN Workshop on Partial Evaluation and Semantics-based Program Manipulation},
year = {2004},
pages = {15--26},
publisher = {ACM Press}
}
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Abstract
Abstract. A powerful application of specialization is to remove interpretative overhead: a language can be implemented with an interpreter, whose performance is then improved by specializing it for a given program source. This approach is only moderately successful with very dynamic languages, where the outcome of each single step can be highly dependent on run-time data. We introduce in the present paper two novel specialization techniques and discuss in particular their potential to close the performance gap between dynamic and static languages: Just-in-time specialization, or specialization by need, introduces the “unlifting” ability for a value to be promoted from run-time to compile-time during specialization – the converse of the lift operator of partial evaluation. Its presence gives an unusual and powerful perspective on the specialization process. Representations are a generalization of the traditional specialization domains, i.e. the compile-time/run-time dichotomy (also called static/dynamic, or “variables known at specialization time”/“variables only known at run time”). They provide a theory of data specialization. These two techniques together shift some traditional problems and limitations of specialization. We present the prototype Psyco for the Python language. 1
