Implementing Implicit Self-Adjusting Computation

Computational problems that involve dynamic data have been an important subject of study in programming languages. Recent advances in self-adjusting computation have developed techniques that enable programs to respond automatically and efficiently to dynamic changes in their inputs. But these techniques have required an explicit programming style, where the programmer must use specific monadic types and primitives to identify, create and operate on data that can change over time. Our paper, “Implicit Self-Adjusting Computation for Purely Functional Programs” (ICFP ’11), describes the theory underlying implicit self-adjusting computation, where the programmer need only annotate the (top-level) input types of the programs to be translated. A type-directed translation rewrites the (purely functional) source program into an explicitly selfadjusting target program. The subject of this talk is a prototype implementation (an extension of MLton) and experimental results that are competitive with explicitly self-adjusting handwritten code.