Parameterized Dataflow (Extended Abstract)
Dominic Duggan (Stevens Institute of Technology), Jianhua Yao (Stevens, Institute of Technology)
TL;DR
This paper introduces a dataflow language with a type and effect system that models actor firing behavior, enabling safe composition and abstraction over firing rates in stream processing applications.
Contribution
It presents a novel type and effect system for dataflow graphs that supports parameterization and safe composition of actors with unspecified firing rates.
Findings
Supports abstraction over actor firing rates
Enables safe composition of dataflow graphs
Captures firing behavior with a type and effect system
Abstract
Dataflow networks have application in various forms of stream processing, for example for parallel processing of multimedia data. The description of dataflow graphs, including their firing behavior, is typically non-compositional and not amenable to separate compilation. This article considers a dataflow language with a type and effect system that captures the firing behavior of actors. This system allows definitions to abstract over actor firing rates, supporting the definition and safe composition of actor definitions where firing rates are not instantiated until a dataflow graph is launched.
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