DagRep.13.5.114.pdf
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Scalable Data Structures (Dagstuhl Seminar 23211)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 13, Issue 5
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-11-27
Cite AsGet BibTex
Gerth Stølting Brodal, John Iacono, László Kozma, Vijaya Ramachandran, and Justin Dallant. Scalable Data Structures (Dagstuhl Seminar 23211). In Dagstuhl Reports, Volume 13, Issue 5, pp. 114-135, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.13.5.114
https://doi.org/10.4230/DagRep.13.5.114
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23211 "Scalable Data Structures". Data structures enable the organization, storage and retrieval of data across a variety of applications. As they are deployed at unprecedented scales, data structures can profoundly affect the efficiency of almost all computational tasks. The study of data structures thus continues to be an important and active area of research with an interplay between data structure design and analysis, developments in computer hardware, and the needs of modern applications. The extended abstracts included in this report give a snapshot of the current state of research on scalable data structures and establish directions for future developments in the field.
Subject Classification
ACM Subject Classification
- Theory of computation → Data structures design and analysis
- Theory of computation → Design and analysis of algorithms
- Theory of computation → Parallel algorithms
Keywords
- algorithms
- big data
- computational models
- data structures
- GPU computing
- parallel computation
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