TY - JOUR T1 - Optimizing Abstract Abstract Machines JF - arXiv:1211.3722 [cs] Y1 - 2013 A1 - Johnson, J. Ian A1 - Labich, Nicholas A1 - Might, Matthew A1 - David Van Horn KW - Computer Science - Programming Languages KW - F.3.2 AB - 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. UR - http://arxiv.org/abs/1211.3722 N1 - Comment: Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 2013 ER -