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Hinted Dictionaries: Efficient Functional Ordered Sets and Maps

Authors Amir Shaikhha, Mahdi Ghorbani, Hesam Shahrokhi

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Author Details

Amir Shaikhha
  • University of Edinburgh, UK
Mahdi Ghorbani
  • University of Edinburgh, UK
Hesam Shahrokhi
  • University of Edinburgh, UK

Acknowledgements

The authors would like to thank Huawei for their support of the distributed data management and processing laboratory at the University of Edinburgh.

Cite AsGet BibTex

Amir Shaikhha, Mahdi Ghorbani, and Hesam Shahrokhi. Hinted Dictionaries: Efficient Functional Ordered Sets and Maps. In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 28:1-28:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ECOOP.2023.28

Abstract

This paper introduces hinted dictionaries for expressing efficient ordered sets and maps functionally. As opposed to the traditional ordered dictionaries with logarithmic operations, hinted dictionaries can achieve better performance by using cursor-like objects referred to as hints. Hinted dictionaries unify the interfaces of imperative ordered dictionaries (e.g., C++ maps) and functional ones (e.g., Adams' sets). We show that such dictionaries can use sorted arrays, unbalanced trees, and balanced trees as their underlying representations. Throughout the paper, we use Scala to present the different components of hinted dictionaries. We also provide a C++ implementation to evaluate the effectiveness of hinted dictionaries. Hinted dictionaries provide superior performance for set-set operations in comparison with the standard library of C++. Also, they show a competitive performance in comparison with the SciPy library for sparse vector operations.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Functional languages
  • Theory of computation → Data structures design and analysis
Keywords
  • Functional Collections
  • Ordered Dictionaries
  • Sparse Linear Algebra

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