Fixed-Point-Oriented Programming: A Concise and Elegant Paradigm

TL;DR

Fixed-Point-Oriented Programming (FPOP) introduces a high-level, declarative paradigm that simplifies the implementation of fixed-point problems like graph algorithms, improving efficiency and maintainability.

Contribution

This paper presents FPOP as a novel programming paradigm that enables concise, expressive, and efficient implementations of fixed-point computations, bridging theory and practice.

Findings

FPOP simplifies complex algorithms, reducing code size significantly.

FPOP enhances maintainability and rapid prototyping.

FPOP demonstrates improved efficiency over traditional paradigms.

Abstract

Fixed-Point-Oriented Programming (FPOP) is an emerging paradigm designed to streamline the implementation of problems involving self-referential computations. These include graph algorithms, static analysis, parsing, and distributed computing-domains that traditionally require complex and tricky-to-implement work-queue algorithms. Existing programming paradigms lack direct support for these inherently fixed-point computations, leading to inefficient and error-prone implementations. This white paper explores the potential of the FPOP paradigm, which offers a high-level abstraction that enables concise and expressive problem formulations. By leveraging structured inference rules and user-directed optimizations, FPOP allows developers to write declarative specifications while the compiler ensures efficient execution. It not only reduces implementation complexity for programmers but also enhances adaptability, making it easier for programmers to explore alternative solutions and optimizations without modifying the core logic of their program. We demonstrate how FPOP simplifies algorithm implementation, improves maintainability, and enables rapid prototyping by allowing problems to be clearly and concisely expressed. For example, the graph distance problem can be expressed in only two executable lines of code with FPOP, while it takes an order of magnitude more code in other paradigms. By bridging the gap between theoretical fixed-point formulations and practical implementations, we aim to foster further research and adoption of this paradigm.