Algorithms and Data Structures for New Models of Computation

In the early days of computer science, the community settled on a simple standard model of computing and a basic canon of general purpose algorithms and data structures suited to that model. With isochronous computing, heterogeneous multiprocessors, flash memory, energy-aware computing, cache and ot...

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Veröffentlicht in:IT professional Jg. 23; H. 1; S. 9 - 15
Hauptverfasser: Black, Paul E., Flater, David, Bojanova, Irena
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington IEEE 01.01.2021
IEEE Computer Society
Schlagworte:
Algorithms
Compacting
Computational modeling
Computer networks
Constraint modelling
Coprocessors
Data models
Data structures
Distributed memory
Distributed processing
Flash memory (computers)
Multiprocessing
Software algorithms
Software development
Software engineering
ISSN:1520-9202, 1941-045X
Online-Zugang:Volltext
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Zusammenfassung:In the early days of computer science, the community settled on a simple standard model of computing and a basic canon of general purpose algorithms and data structures suited to that model. With isochronous computing, heterogeneous multiprocessors, flash memory, energy-aware computing, cache and other anisotropic memory, distributed computing, streaming environments, functional languages, graphics coprocessors, etc., the basic canon of algorithms and data structures is not enough. Software developers know of real-world constraints and new models of computation and use them to design effective algorithms and data structures. These constraints motivate the development of elegant algorithms with broad utility. As examples, we present four algorithms that were motivated by specific hardware nuances, but are generally useful: reservoir sampling, majority of a stream, B-heap, and compacting an array in \Theta (\log n)Θ(logn) time.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1520-9202
1941-045X
DOI:10.1109/MITP.2020.3042858