Special Issue "High Performance Computing for Big Data"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 March 2016)

Special Issue Editor

Guest Editor
Dr. José Luis Vázquez-Poletti

Departamento de Arquitectura de Computadores y Automática, Facultad de Informática, Universidad Complutense de Madrid
Website | E-Mail
Interests: cloud computing; grid computing; SLA; applications; high performance computing

Special Issue Information

Dear Colleagues,

Big Data is, right now one of the hottest topics in computing research. This is because of:

- the numerous challenges that include (and are not limited to) capture, search, storage, sharing, transfer, representation and privacy of the data;

- and the wide spectrum of areas covered, that range from Bioinformatics to Space Science, and are a research challenge by themselves.

New technologies and algorithms have emerged from Big Data to efficiently manage and process great quantities of data within reasonable elapsed times. However, there are computing barriers that cannot be crossed without the proper resources.

The many ways that High Performance Computing can be delivered for facing Big Data challenges offer a wide spectrum of research opportunities. From FPGAs to cloud computing, technologies and algorithms can be brought to a whole different level and foster incredible insights from massive information repositories.

The papers accepted for publication of the present Special Issue covers both fundamental issues and new concepts related to the application of High Performance Computing to the Big Data area.

Dr. José Luis Vázquez-Poletti
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. Computers is an international peer-reviewed open access quarterly 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 350 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.


  • big data
  • high performance computing
  • cloud computing
  • applications

Published Papers (1 paper)

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Open AccessArticle A New Scalable, Distributed, Fuzzy C-Means Algorithm-Based Mobile Agents Scheme for HPC: SPMD Application
Computers 2016, 5(3), 14; doi:10.3390/computers5030014
Received: 4 May 2016 / Revised: 29 June 2016 / Accepted: 1 July 2016 / Published: 11 July 2016
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The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by the way to
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The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by the way to ensure the scalability and the efficiency of this method. Additionally, the proposed distributed method integrates a new communication mechanism to ensure HPC (High Performance Computing) of parallel programs as distributed one, by means of cooperative mobile agents team that uses its asynchronous communication ability to achieve that. This mobile agents team implements the distributed method of the Fuzzy C-Means Algorithm (DFCM) and performs the Big Data classification in the distributed system. The paper shows the proposed scheme and its assigned DFCM algorithm and presents some experimental results that illustrate the scalability and the efficiency of this distributed method. Full article
(This article belongs to the Special Issue High Performance Computing for Big Data)

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