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POPL
2008
ACM

Imperative self-adjusting computation

9 years 2 months ago
Imperative self-adjusting computation
Self-adjusting computation enables writing programs that can automatically and efficiently respond to changes to their data (e.g., inputs). The idea behind the approach is to store all data that can change over time in modifiable references and to let computations construct traces that can drive change propagation. After changes have occurred, change propagation updates the result of the computation by re-evaluating only those expressions that depend on the changed data. Previous approaches to self-adjusting computation require that modifiable references be written at most once during execution--this makes the model applicable only in a purely functional setting. In this paper, we present techniques for imperative self-adjusting computation where modifiable references can be written multiple times. We define a language SAIL (Self-Adjusting Imperative Language) and prove consistency, i.e., that change propagation and from-scratch execution are observationally equivalent. Since SAIL pro...
Umut A. Acar, Amal Ahmed, Matthias Blume
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2008
Where POPL
Authors Umut A. Acar, Amal Ahmed, Matthias Blume
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