Parallel Processing and High Performance Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4408

Special Issue Editors


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Guest Editor
Department of Computer Technology, University of Alicante, 03690 Alicante, Spain
Interests: optimization; parallelism; high-performance computing; CAD/CAM systems; metaheuristics; computer arithmetic; industrial applications of computing
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Guest Editor
Department of Computer Technology. University of Alicante, 03690 Alicante, Spain
Interests: CAD/CAM; footwear; sport biomechanics; computational geometry; high-performance computing

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Guest Editor
Department of Computer Technology, University of Alicante, 03690 Alicante, Spain
Interests: HW/SW codesign; HW/SW hardening; radiation hardening of embedded systems; neuroprosthetics; neuroengineering

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Guest Editor
Department of Computer Engineering, Miguel Hernandez University, 03202 Elche, Spain
Interests: parallel computing; parallel programming; high-performance computing; parallel and distributed computing; GPU programming; OpenMP; MPI; nonlinear systems; distributed computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, a growing trend has emerged addressed at improving data processing. From big data to artificial intelligence, strong requirements have arisen with regard to decreasing computational time while maintaining solutions’ quality.

These require a high computational power, and therefore it is necessary to provide high-performance strategies, from algorithm optimization and parallelization to high-performance architectures.

This Special Issue on Parallel Processing and High-Performance Computing aims to contribute recent advances in hardware and software environments for optimizing computing, parallel strategies, and high-performance implementations of algorithms and methods, and their application to industry, engineering, medical science, and other disciplines.

Prof. Antonio Jimeno-Morenilla
Dr. Jose-Luis Sanchez-Romero
Dr. Antonio Martínez-Álvarez
Dr. Héctor Migallón
Guest Editors

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Keywords

  • Parallel processing
  • Parallel architectures and implementation
  • Software and hardware optimization
  • FPGA architectures
  • GPU processing
  • OpenCL and CUDA Architectures
  • OpenMP and MPI architectures
  • Optimization of industrial and engineering processes
  • Applications of parallel and high-performance computing

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Published Papers (1 paper)

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Research

15 pages, 632 KiB  
Article
Distributed Deep Learning: From Single-Node to Multi-Node Architecture
by Jean-Sébastien Lerat, Sidi Ahmed Mahmoudi and Saïd Mahmoudi
Electronics 2022, 11(10), 1525; https://doi.org/10.3390/electronics11101525 - 10 May 2022
Cited by 2 | Viewed by 2317
Abstract
During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure [...] Read more.
During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. The impact of computational resources (CPU and GPU) is also discussed since the GPU is known to speed up computations. Experimental results show that the local parallelism impacts the global speedup of the DDL depending on the neural model complexity and the size of the dataset. Moreover, our approach achieves a better speedup than Horovod. Full article
(This article belongs to the Special Issue Parallel Processing and High Performance Computing)
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