Tuple spaces implementations and their efficiency

TL;DR

This paper analyzes various tuple space implementations, compares their performance across different case studies, and offers insights and recommendations to improve their efficiency and adoption in parallel and distributed computing.

Contribution

It provides an extensive analysis and comparison of existing tuple space implementations, highlighting their strengths, weaknesses, and guiding future development efforts.

Findings

Different implementations excel in various aspects like communication and data manipulation.

Performance varies significantly depending on the case study and implementation.

Recommendations for building more effective tuple space systems are proposed.

Abstract

Among the paradigms for parallel and distributed computing, the one popularized with Linda, and based on tuple spaces, is one of the least used, despite the fact of being intuitive, easy to understand and to use. A tuple space is a repository, where processes can add, withdraw or read tuples by means of atomic operations. Tuples may contain different values, and processes can inspect their content via pattern matching. The lack of a reference implementation for this paradigm has prevented its widespread. In this paper, first we perform an extensive analysis of a number of actual implementations of the tuple space paradigm and summarise their main features. Then, we select four such implementations and compare their performances on four different case studies that aim at stressing different aspects of computing such as communication, data manipulation, and cpu usage. After reasoning on strengths and weaknesses of the four implementations, we conclude with some recommendations for future work towards building an effective implementation of the tuple space paradigm.