Over the past thirty years, there has been significant progress in developing general-purpose, language-based approaches to incremental computation, which aims to efficiently update the result of a computation when an input is changed. A key design challenge in such approaches is how to provide efficient incremental support for a broad range of programs. In this paper, we argue that first-class names are a critical linguistic feature for efficient incremental computation. Names identify computations to be reused across differing runs of a program, and making them first class gives programmers a high level of control over reuse. We demonstrate the benefits of names by presenting Nominal Adapton, an ML-like language for incremental computation with names. We describe how to use Nominal Adapton to efficiently incrementalize several standard programming patterns---including maps, folds, and unfolds---and show how to build efficient, incremental probabilistic trees and tries. Since Nominal Adapton's implementation is subtle, we formalize it as a core calculus and prove it is from-scratch consistent, meaning it always produces the same answer as simply re-running the computation. Finally, we demonstrate that Nominal Adapton can provide large speedups over both from-scratch computation and Adapton, a previous state-of-the-art incremental system.

[ .pdf ]

@inproceedings{hammer14nominal,
  author = {Matthew Hammer and Kyle Headley and Nicholas Labich and Jeffrey S. Foster and Michael Hicks and David Van Horn and Jana Dunfield},
  title = {Incremental Computation with Names},
  booktitle = {Proceedings of the {ACM} Conference on Object-Oriented Programming Languages, Systems, and Applications (OOPSLA)},
  month = oct,
  year = 2015
}

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