Optimizing abstract abstract machines

Author:

Glaze Dionna1,Labich Nicholas1,Might Matthew2,Van Horn David1

Affiliation:

1. Northeastern University, Boston, MA, USA

2. University of Utah, Salt Lake City, UT, USA

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

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