Optimizing Abstract Abstract Machines
Programming Languages
2013-07-25 v4
Abstract
The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.
Keywords
Cite
@article{arxiv.1211.3722,
title = {Optimizing Abstract Abstract Machines},
author = {J. Ian Johnson and Nicholas Labich and Matthew Might and David Van Horn},
journal= {arXiv preprint arXiv:1211.3722},
year = {2013}
}
Comments
Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 2013