Adapton: Composable, Demand-Driven Incremental Computation

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Many researchers have proposed programming languages that support incremental computation (IC), which allows programs to be efficiently re-executed after a small change to the input. However, existing implementations of such languages have two important drawbacks. First, recomputation is oblivious to specific demands on the program output; that is, if a program input changes, all dependencies will be recomputed, even if an observer no longer requires certain outputs. Second, programs are made incremental as a unit, with little or no support for reusing results outside of their original context, e.g., when reordered. To address these problems, we present lambdaCDDIC, a core calculus that applies a demand-driven semantics to incremental computation, tracking changes in a hierarchical fashion in a novel demanded computation graph. lambdaCDDIC also formalizes an explicit separation between inner, incremental computations and outer observers. This combination ensures lambdaCDDIC programs only recompute computations as demanded by observers, and allows inner computations to be composed more freely. We describe an algorithm for implementing lambdaCDDIC efficiently, and we present AdaptOn, a library for writing lambdaCDDIC-style programs in OCaml. We evaluated AdaptOn on a range of benchmarks, and found that it provides reliable speedups, and in many cases dramatically outperforms prior state-of-the-art IC approaches.