Monoidal Streams for Dataflow Programming
Elena Di Lavore, Giovanni de Felice, Mario Rom\'an
TL;DR
This paper introduces monoidal streams, a categorical framework that generalizes causal stream functions to model dataflow programming with process theories, enabling semantics for signal flow graphs and stochastic dataflow languages.
Contribution
It presents monoidal streams as a new categorical structure that extends causal stream functions to symmetric monoidal categories, providing a foundation for dataflow semantics.
Findings
Monoidal streams form a feedback monoidal category.
They can interpret signal flow graphs.
Application to a stochastic dataflow language.
Abstract
We introduce monoidal streams: a generalization of causal stream functions to monoidal categories. In the same way that streams provide semantics to dataflow programming with pure functions, monoidal streams provide semantics to dataflow programming with theories of processes represented by a symmetric monoidal category. At the same time, monoidal streams form a feedback monoidal category, which can be used to interpret signal flow graphs. As an example, we study a stochastic dataflow language.
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Taxonomy
TopicsFormal Methods in Verification · Logic, Reasoning, and Knowledge · Computability, Logic, AI Algorithms