DagRep.11.1.1.pdf
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Scalable Data Structures (Dagstuhl Seminar 21071)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 11, Issue 1
Volume: Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2021-07-14
Cite As Get BibTex
Gerth Stølting Brodal, John Iacono, Markus E. Nebel, and Vijaya Ramachandran. Scalable Data Structures (Dagstuhl Seminar 21071). In Dagstuhl Reports, Volume 11, Issue 1, pp. 1-23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/DagRep.11.1.1
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21071 "Scalable Data Structure". Even if the field of data structures is quite mature, new trends and limitations in computer hardware together with the ever-increasing amounts of data that need to be processed raise new questions with respect to efficiency and continuously challenge the existing models of computation. Thermal and electrical power constraints have caused technology to reach "the power wall" with stagnating single processor performance, meaning that all nontrivial applications need to address scalability with multiple processors, a memory hierarchy and other communication challenges. Scalable data structures are pivotal to this process since they form the backbone of the algorithms driving these applications. The extended abstracts included in this report contain both recent state of the art advances and lay the foundation for new directions within data structures research.
Subject Classification
ACM Subject Classification
- Theory of computation → Data structures design and analysis
- Theory of computation → Design and analysis of algorithms
Keywords
- algorithms
- big data
- data structures
- GPU computing
- large data sets
- models of computation
- parallel algorithms
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