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Imperative Self-Adjusting Computation Umut A. Acar Amal Ahmed Matthias Blume

Summary: Imperative Self-Adjusting Computation
Umut A. Acar Amal Ahmed Matthias Blume
Toyota Technological Institute at Chicago
Self-adjusting computation enables writing programs that can au-
tomatically 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 com-
putation 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 func-
tional 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 Lan-
guage) and prove consistency, i.e., that change propagation and


Source: Ahmed, Amal - School of Informatics, Indiana University


Collections: Computer Technologies and Information Sciences