Self-Adjusting Stack Machines

Matthew A. Hammer, Georg Neis, Yan Chen and Umut A. Acar


Self-adjusting computation offers a language-based approach to writing programs that automatically respond to dynamically changing data. Recent work made significant progress in developing sound semantics and associated implementations of self-adjusting computation for high-level, functional languages. These techniques, however, do not address issues that arise for low-level languages, i.e., stack-based imperative languages that lack strong type systems and automatic memory management.

In this paper, we describe techniques for self-adjusting computation which are suitable for low-level languages. Necessarily, we take a different approach than previous work: instead of starting with a high-level language with additional primitives to support self-adjusting computation, we start with a low-level intermediate language, whose semantics is given by a stack-based abstract machine. We prove that this semantics is sound: it always updates computations in a way that is consistent with full reevaluation. We give a compiler and runtime system for the intermediate language used by our abstract machine. We present an empirical evaluation that shows that our approach is efficient in practice, and performs favorably compared to prior proposals.


 author = "Matthew Hammer and Georg Neis and Yan Chen and Umut A. Acar",
 title = "Self-Adjusting Stack Machines",
 booktitle = {ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA)},
 year = 2011


  • The OOPSLA 2011 paper
  • The accompanying technical report contains complete proofs, as well as some additional subsections that didn't fit into the conference version.