Journal ArticleDOI
A Survey of Communication Performance Models for High-Performance Computing
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TLDR
In this article, the authors present the state-of-the-art in analytic communication performance models, providing sufficiently detailed descriptions of particularly noteworthy efforts, as well as future directions for future research in the area of analytical communication performance modeling.Abstract:
This survey aims to present the state of the art in analytic communication performance models, providing sufficiently detailed descriptions of particularly noteworthy efforts. Modeling the cost of communications in computer clusters is an important and challenging problem. It provides insights into the design of the communication pattern of parallel scientific applications and mathematical kernels and sets a clear ground for optimization of their deployment in the increasingly complex high-performance computing infrastructure. The survey provides background information on how different performance models represent the underlying platform and shows the evolution of these models over time from early clusters of single-core processors to present-day multi-core and heterogeneous platforms. Prospective directions for future research in the area of analytic communication performance modeling conclude the survey.read more
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Survey, comparison and research challenges of IoT application protocols for smart farming
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Fog computing: A taxonomy, systematic review, current trends and research challenges
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The internet of things security: A survey encompassing unexplored areas and new insights
Abiodun Esther Omolara,Abiodun Esther Omolara,Abdullah Alabdulatif,Oludare Isaac Abiodun,Oludare Isaac Abiodun,Moatsum Alawida,Moatsum Alawida,Abdulatif Alabdulatif,Wafa' Hamdan Alshoura,Humaira Arshad,Humaira Arshad +10 more
TL;DR: In this paper, a systematic literature review of over 200 articles is presented to provide new insights into the security of IoTs, taking cognizant of its social, economic, technical and legal implications, which will be beneficial to researchers, manufacturers, individuals, organizations and governments.
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Neural network quantization in federated learning at the edge
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