Affiliation:
1. University of Cambridge, UK
2. Intel Labs, USA
Abstract
Causal commutative arrows (CCA) extend arrows with additional constructs and laws that make them suitable for modelling domains such as functional reactive programming, differential equations and synchronous dataflow.
Earlier work has revealed that a syntactic transformation of CCA computations into normal form can result in significant performance improvements, sometimes increasing the speed of programs by orders of magnitude.
In this work we reformulate the normalization as a type class instance and derive optimized observation functions via a specialization to stream transformers to demonstrate that the same dramatic improvements can be achieved without leaving the language.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design,Software
Cited by
4 articles.
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1. This Is Driving Me Loopy: Efficient Loops in Arrowized Functional Reactive Programs;Proceedings of the 16th ACM SIGPLAN International Haskell Symposium;2023-08-30
2. Functional Reactive Programming, restated;Proceedings of the 21st International Symposium on Principles and Practice of Programming Languages 2019;2019-10-07
3. Synthesizing functional reactive programs;Proceedings of the 12th ACM SIGPLAN International Symposium on Haskell - Haskell 2019;2019
4. STCLang: state thread composition as a foundation for monadic dataflow parallelism;Proceedings of the 12th ACM SIGPLAN International Symposium on Haskell - Haskell 2019;2019