research-article

Adaptive parallel applications: from shared memory architectures to fog computing (2002–2022)

Authors:

Guilherme Galante,

Rodrigo da Rosa RighiAuthors Info & Claims

Cluster Computing, Volume 25, Issue 6

Pages 4439 - 4461

https://doi.org/10.1007/s10586-022-03692-2

Published: 01 December 2022 Publication History

Abstract

The evolution of parallel architectures points to dynamic environments where the number of available resources or configurations may vary during the execution of applications. This can be easily observed in grids and clouds, but can also be explored in clusters and multiprocessor architectures. Over more than two decades, several research initiatives have explored this characteristic by parallel applications, enabling the development of adaptive applications that can reconfigure the number of processes/threads and their allocation to processors to cope with varying workloads and changes in the availability of resources in the system. Despite the long history of development of solutions for adaptability for parallel architectures, there is no literature reviewing these efforts. In this context, the goal of this paper is to present the state of-the-art on adaptability from resource and application perspectives, ranging from shared memory architectures, clusters, and grids, to virtualized resources in cloud and fog computing in the last twenty years (2002-2022). A comprehensive analysis of the leading research initiatives in the field of adaptive parallel applications can provide the reader with an understanding of the essential concepts of development in this area.

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Adaptive parallel applications: from shared memory architectures to fog computing (2002–2022)

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cover image Cluster Computing

Cluster Computing Volume 25, Issue 6

Dec 2022

885 pages

ISSN:1386-7857

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2022

Accepted: 13 July 2022

Revision received: 01 June 2022

Received: 01 April 2022

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Finnerty PPosner JBürger JTakaoka LKanzaki T(2024)On the Performance of Malleable APGAS Programs and Batch Job SchedulersSN Computer Science10.1007/s42979-024-02641-75:4Online publication date: 27-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02641-7
Posner JGoebel RFinnerty P(2024)Evolving APGAS Programs: Automatic and Transparent Resource Adjustments at RuntimeAsynchronous Many-Task Systems and Applications10.1007/978-3-031-61763-8_15(154-165)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1007/978-3-031-61763-8_15
Finnerty PTakaoka LKanzaki TPosner J(2023)Malleable APGAS Programs and Their Support in Batch Job SchedulersEuro-Par 2023: Parallel Processing Workshops10.1007/978-3-031-48803-0_8(89-101)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-48803-0_8

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