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Appl. Sci. HPC 2021 : Applied Sciences Special Issue High-Performance Computing and Supercomputing

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Link: https://www.mdpi.com/journal/applsci/special_issues/High-Performance_Computing_Supercomputing
 
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Submission Deadline Jan 31, 2021
 

Call For Papers

Special Issue "High-Performance Computing and Supercomputing" (https://mdpi.com/si/54683)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2021.

SPECIAL ISSUE EDITOR

Prof. Dr. Jose Miguel-Alonso
Guest Editor
Department of Computer Architecture and Technology, The University of the Basque Country UPV/EHU
Interests: architecture of HPC systems, focused mainly on the interconnection network; performance evaluation; simulation; scheduling in HPC environments

SPECIAL ISSUE INFORMATION

Dear Colleagues,

Now more than ever, supercomputing systems are essential to the advancement of science and engineering. Every semester we see impressive advances in the Top500/Green500 charts, achieved through the use of high-performance interconnects linking thousands of high-performance nodes, often supplemented by high-performance accelerators/coprocessors. These large-scale systems are rarely used by a single application, but are shared by multiple concurrent users/applications. Additionally, despite implementing different measures to achieve energy efficiency, top-of-the-list supercomputers consume several GW. The range of applications for which supercomputers are used is large (biosciences, Earth sciences, energy, materials, computer architecture, and many more) and we are seeing a convergence between these “classic” and newer applications based on what is known as “big data”—the analysis of massive amounts of data in order to extract knowledge from them.

This reality motivates the topics of this Special Issue on High-Performance Computing and Supercomputing of the journal Applied Sciences: current trends and emerging technologies in the architecture of HPC systems, including data storage systems, interconnection networks, accelerators/coprocessors, and energy efficiency measures; task management and scheduling in HPC systems seeking to optimize performance and energy efficiency; novel uses of HPC with special focus on HPC–big data convergence; programming and run-time environments for HPC.

Prof. Dr. Jose Miguel-Alonso
Guest Editor


Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

KEYWORDS

architecture of HPC and supercomputing systems
storage systems
interconnection networks
accelerators and co-processors
task management and scheduling
energy efficiency
HPC–big data convergence
programming languages and run-time systems

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