High Performance Computing Technologies and Application Evolution

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 2029

Special Issue Editors


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Guest Editor
Jozef Stefan Institue, University of Nova Gorica, Nova Gorica, Slovenia
Interests: particle and astroparticle physics; distributed and high-performance computing

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Guest Editor
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Interests: soft computing methods; distributed processing and their applications

Special Issue Information

Dear Colleagues,

The main topic of this issue will be on high-performance computing, rapidly evolving technologies and computing architectures, and increasing demand for extreme computation, data processing, high-performance data analysis, and artificial intelligence. High-performance computing is evolving from systems dedicated to pure computation or simulation to general purpose infrastructure also suitable for fast data processing, machine learning, data store and preservation, while the HPC centers are being upgraded with hyper-converged networks connecting them to other research or public facilities. High-performance computational techniques and algorithms are rapidly evolving and are being used and reused by research communities that historically did not rely on HPC, as well as by industry and public sector.

With this perspective, the Special Issue aims to contribute to the field presenting the most relevant advances in this area.

The following are some of the topics proposed for this Special Issue (but papers need not be limited to these):

  • Design and architecture of novel CPU, GPU, FGPA, and other computational units for future high-performance systems
  • Innovative architectures of HPC systems, novel approaches to scalability in computation and data processing, energy efficiency
  • Algorithms for energy efficient computation at exa-scale
  • Application development and porting to novel scalable architectures, high-performance toolkits, programming abstraction for different architectures
  • Resource federation, integration with hyper-converged networks, federated authentication and authorization through IdP, security
  • Large scale HPC application integration with fog, edge, distributed computing, clouds, virtualization and containerization
  • Application use-cases, initiatives, project on exa-scale compute and data projects
  • Quantum Computing technologies and applications

Prof. Dr. Andrej Filipčič
Prof. Uroš Lotrič
Guest Editors

Manuscript Submission Information

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Keywords

  • novel high-performance systems and architectures
  • scalable applications and energy efficient algorithms
  • HPC distributed computing and data processing
  • exa-scale HPC and beyond
  • novel HPC projects, communities and applications
  • quantum computing

Published Papers (1 paper)

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Research

35 pages, 1111 KiB  
Article
Performance Assessment of Using Docker for Selected MPI Applications in a Parallel Environment Based on Commodity Hardware
by Tomasz Kononowicz and Paweł Czarnul
Appl. Sci. 2022, 12(16), 8305; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168305 - 19 Aug 2022
Cited by 2 | Viewed by 1418
Abstract
In the paper, we perform detailed performance analysis of three parallel MPI applications run in a parallel environment based on commodity hardware, using Docker and bare-metal configurations. The testbed applications are representative of the most typical parallel processing paradigms: master–slave, geometric Single Program [...] Read more.
In the paper, we perform detailed performance analysis of three parallel MPI applications run in a parallel environment based on commodity hardware, using Docker and bare-metal configurations. The testbed applications are representative of the most typical parallel processing paradigms: master–slave, geometric Single Program Multiple Data (SPMD) as well as divide-and-conquer and feature characteristic computational and communication schemes. We perform analysis selecting best configurations considering various optimization flags for the applications and best execution times and speed-ups in terms of the number of nodes and overhead of the virtualized environment. We have concluded that for the configurations giving the shortest execution times the overheads of Docker versus bare-metal for the applications are as follows: 7.59% for master–slave run using 64 processes (number of physical cores), 15.30% for geometric SPMD run using 128 processes (number of logical cores) and 13.29% for divide-and-conquer run using 256 processes. Finally, we compare results obtained using gcc V9 and V7 compiler versions. Full article
(This article belongs to the Special Issue High Performance Computing Technologies and Application Evolution)
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