Functional reactive programming

Functional reactive programming (FRP) is a programming paradigm for reactive programming (asynchronous dataflow programming) using the building blocks of functional programming (e.g. map, reduce, filter). FRP has been used for programming graphical user interfaces (GUIs), robotics, games, and music, aiming to simplify these problems by explicitly modeling time. The original formulation of functional reactive programming can be found in the ICFP 97 paper Functional Reactive Animation by Conal Elliott and Paul Hudak. FRP has taken many forms since its introduction in 1997. One axis of diversity is discrete vs. continuous semantics. Another axis is how FRP systems can be changed dynamically. The earliest formulation of FRP used continuous semantics, aiming to abstract over many operational details that are not important to the meaning of a program. The key properties of this formulation are: Modeling values that vary over continuous time, called "behaviors" and later "signals". Modeling "events" which have occurrences at discrete points in time. The system can be changed in response to events, generally termed "switching." The separation of evaluation details such as sampling rate from the reactive model. This semantic model of FRP in side-effect free languages is typically in terms of continuous functions, and typically over time. This formulation is also referred to as denotative continuous time programming (DCTP). Formulations such as Event-Driven FRP and versions of Elm prior to 0.17 require that updates are discrete and event-driven. These formulations have pushed for practical FRP, focusing on semantics that have a simple API that can be implemented efficiently in a setting such as robotics or in a web-browser. In these formulations, it is common that the ideas of behaviors and events are combined into signals that always have a current value, but change discretely. It has been pointed out that the ordinary FRP model, from inputs to outputs, is poorly suited to interactive programs.

Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch

Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...

SPRINGER2023

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch

The performance of programs executed on heterogeneous parallel platforms largely depends on the design choices regarding how to partition the processing on the various different processing units. In other words, it depends on the assumptions and parameters ...

MDPI2022

Design Space Exploration Problem Formulation for Dataflow Programs on Heterogeneous Architectures, 2016 IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC)

Marco Mattavelli, Endri Bezati, Nicolas Zufferey, Malgorzata Maria Michalska

The implementations of signal processing systems on the emerging many-core or multi-core processing platforms require to solve a very difficult problem: how to partition and schedule the processing tasks according to given optimization functions such as da ...

2016