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Self-adjusting Computation with Delta ML

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Book cover Advanced Functional Programming (AFP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5832))

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Abstract

In self-adjusting computation, programs respond automatically and efficiently to modifications to their data by tracking the dynamic data dependences of the computation and incrementally updating the output as needed. In this tutorial, we describe the self-adjusting-computation model and present the language ΔML (Delta ML) for writing self-adjusting programs.

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Author information

Authors and Affiliations

  1. Toyota Technological Institute, Chicago, IL, USA

    Umut A. Acar

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Ruy Ley-Wild

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  1. Umut A. Acar
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  2. Ruy Ley-Wild
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Editors and Affiliations

  1. Institute for Computing and Information Sciences, Radboud University Nijmegen, Heijendaalseweg 135, 6525, Nijmegen, AJ, The Netherlands

    Pieter Koopman  & Rinus Plasmeijer  & 

  2. Department of Information and Computing Sciences, Utrecht Universiy, Padualaan 14, 3584, Utrecht, CH, The Netherlands

    Doaitse Swierstra

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Acar, U.A., Ley-Wild, R. (2009). Self-adjusting Computation with Delta ML. In: Koopman, P., Plasmeijer, R., Swierstra, D. (eds) Advanced Functional Programming. AFP 2008. Lecture Notes in Computer Science, vol 5832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04652-0_1

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  • DOI: https://doi.org/10.1007/978-3-642-04652-0_1

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