A Software Architecture Based on Coarse-Grained Self-Adjusting Computations

Stefan Wehr

In Proc. of FUNARCH 2023. Seattle, WA, USA. ACM, 2023.

Abstract

Ensuring that software applications present their users the most recent version of data is not trivial. Self-adjusting computations are a technique for automatically and efficiently recomputing output data whenever some input changes.

This article describes the software architecture of a large, commercial software system built around a framework for coarse-grained self-adjusting computations in Haskell. It discusses advantages and disadvantages based on longtime experience. The article also presents a demo of the system and explains the API of the framework.

Bibtex

@INPROCEEDINGS{Wehr2023,
  author = {Stefan Wehr},
  title = {A Software Architecture Based on Coarse-Grained Self-Adjusting Computations},
  booktitle = {Proc. of FUNARCH 2023},
  year = 2023,
  address = {Seattle, WA, USA},
  publisher = {{ACM}},
  doi = {https://doi.org/10.1145/3609025.3609481}
}

Resources

• Preprint
• ACM version
• Implementation