International Journal of High Performance Computing and Networking

This journal also publishes Open Access articles

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Editor in Chief: Prof. Kuan-Ching Li
ISSN online: 1740-0570
ISSN print: 1740-0562
6 issues per year
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IJHPCN addresses the most innovative developments in high-performance computing and networking such as information and system architectures, grid- and web-based information management and infrastructures, data storage, management, analysis and visualisation, advanced networking with applications, scalable parallel computing, cluster and grid computing, distributed systems, and high performance scientific and engineering computing with applications.


 Topics covered include

  • Hardware/software support
  • Architecture, compilers, hardware, tools
  • Programming environments, languages, algorithms
  • Operation system support, I/O, memory issues
  • Parallelisation, scheduling, load balancing
  • Problem solving, performance modelling/evaluation
  • Large-scale workflow management
  • Cluster, grid, peer-to-peer, GPU, multicore and cloud computing
  • Virtualisation technologies
  • Web and service computing, social computing, Internet of Things
  • Energy efficient computing infrastructure
  • Complex computing system infrastructure management, cyber security
  • Autonomic computing; data management/distributed data systems
  • Big data infrastructure, storage and computation management
  • Advanced and next-generation networking technologies

    More topics...

    Hardware and Software Support for HPCN

    • Including architecture, compilers, hardware, tools, programming environments, languages and algorithms, operation system support, I/O and memory issues, etc.

    Techniques and Algorithms for HPCN

    • Including new numerical and non-numerical techniques for parallelisation, scheduling and load balancing, problem solving environments, performance modelling and evaluation, etc.

    Cluster and Grid Computing

    • Including cluster middleware, managing clusters, grid computing and clusters, etc.

    Advanced Networking with Applications

    • Including network architecture, protocols, mobile computing, wireless networks, optical networks, performance evaluation, routing, etc.

    Web and Grid based Computing

    • Including distributed/federated information and knowledge management, information brokerage, virtual enterprises, clearing houses, electronic business, cooperation coordination, tele-work, etc.

    Related Applications

    • Including computational fluid dynamics and mechanics, material sciences, space, weather, climate systems and global changes, computational environment and energy systems, computational ocean and earth sciences, combustion system simulation, computational chemistry, computational physics, bioinformatics and computational biology, medical applications, transportation systems simulations, combinatorial and global optimisation problems, structural engineering, computational electromagnetics, data mining, computer graphics, virtual reality and multimedia, computational finance, semiconductor technology, electronic circuits and system design, signal and image processing, and dynamic systems, etc.



    More on this journal...

    The objective of IJHPCN is to provide an outstanding channel for academics, professionals, educators and policy makers working in the field to contribute and to disseminate innovative and important new work in high-performance computing and advanced networking.


    Scientists, engineers, researchers, educators, programmers, system administrators, managers, and industrial professionals.


    IJHPCN provides an international forum to report, discuss and exchange experimental or theoretical results, novel designs, work-in-progress, experience, case studies, and trend-setting ideas. Papers should be of a quality that advances the state of the art in the field, bringing together the latest advances in technology, science, research, application, and education, and stimulating future trends in the areas.



    Browse issues

    Vol. 10
    Vol. 9
    Vol. 8
    Vol. 7
    Vol. 6
    Vol. 5


    More volumes...


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     IJHPCN is indexed in:


     IJHPCN is listed in:


    Editor in Chief

    • Li, Kuan-Ching, Providence University, Taiwan

        Associate Editors

        • Chen, Xiaofeng, Xidian University, China
        • Couturier, RaphaŽl, University of Franche-Comte, France
        • Fowler, Scott, Linkping University, Sweden
        • Jararweh, Yaser, Jordan University of Science and Technology, Jordan
        • Jiang, Hai, Arkansas State University, USA
        • Peng, Shao-Liang, National University of Defense Technology, China
        • Pop, Florin, University Politehnica of Bucharest, Romania
        • Sarkar, Nurul, Auckland University of Technology, New Zealand
        • Yang, Laurence T., St Francis Xavier University, Canada

        Advisory Board

        • Akl, Selim, Queen's University, Canada
        • Cao, Jiannong, Hong Kong Polytechnic University, Hong Kong SAR, China
        • Chapman, Barbara, Stony Brook University, USA
        • Dongarra, Jack, University of Tennessee Knoxville, USA
        • Fox, Geoffrey Charles, Indiana University, USA
        • Gao, Guang R., University of Delaware, USA
        • Gaudiot, Jean-Luc, University of California Irvine, USA
        • Gropp, William D., University of Illinois Urbana-Champaign, USA
        • Guo, Minyi, Shanghai Jiao Tong University, China
        • Jia, Xiaohua, City University of Hong Kong, Hong Kong SAR, China
        • Jin, Hai, Huazhong University of Science and Technology, China
        • Ni, Lionel M., University of Macau, Macao
        • Pedrycz, Witold, University of Alberta, Canada
        • Sahni, Sartaj, University of Florida, USA
        • Sun, Xian-He, Illinois Institute of Technology, USA
        • Valero, Mateo, Universidad Politecnica de Catalunya, Spain
        • Wu, Jie, Temple University, USA
        • Xu, Zhiwei, Chinese Academy of Sciences, China
        • Zima, Hans, California Institute of Technology, USA
        • Zomaya, Albert, University of Sydney, Australia

        Editorial Board Members

        • Au, Man Ho, Hong Kong Polytechnic University, Hong Kong SAR, China
        • Castiglione, Aniello, University of Salerno, Italy
        • Cheng, Albert M.K., University of Houston, USA
        • Choo, Kim-Kwang Raymond, University of Texas at San Antonio, USA
        • D'Agostino, Daniele, Italian National Research Council, Italy
        • Dobre, Ciprian, University Politehnica of Bucharest, Romania
        • Huang, Runhe, Hosei University, Japan
        • Katz, Daniel S., University of Illinois, USA
        • Lai, Kuan-Chou, National Taichung University, Taiwan
        • Lau, Francis C.M., The University of Hong Kong, Hong Kong SAR, China
        • Li, Kenli, Hunan University, China
        • Liu, Yang, Beijing Institute of Technology, China
        • Mao, Bo, Xiamen University, China
        • Marino, Mario Donato, Leeds Beckett University, UK
        • Navaux, Philippe O. A., Federal University of Rio Grande do Sul (UFRGS), Brazil
        • Qi, Heng, Dalian University of Technology, China
        • Rana, Omer F., Cardiff University, UK
        • Ren, Fenghui, University of Wollongong, Australia
        • Sens, Pierre, LIP6 - REGAL Team, France
        • Talia, Domenico, Universit della Calabria, Italy
        • Thampi, Sabu M., Indian Institute of Information Technology and Management - Kerala (IIITM-K), India
        • Tumeo, Antonino, Pacific Northwest National Laboratory, USA
        • Venticinque, Salvatore, Second University of Naples, Italy
        • Wang, Cho-Li, The University of Hong Kong, Hong Kong SAR, China
        • Wang, Guojun, Guangzhou University, China
        • Westphall, Carlos Becker, Federal University of Santa Catarina, Brazil
        • Xue, Jingling, University of New South Wales, Australia
        • Yang, Chao-Tung, Tunghai University, Taiwan
        • Zhao, Wei, University of Macau, Macao
        • Znati, Taieb, University of Pittsburgh, USA
        • Zou, Deqing, Huazhong University of Science and Technology, China


        A few essentials for publishing in this journal


        • Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
        • Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
        • All our articles go through a double-blind review process.
        • All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF)†is available.
        • There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.


        Submission process


        All articles for this journal must be submitted using our online submissions system.

        Read our preparing and submitting articles page.



        Journal news

        • Forthcoming paper


          Title: Evaluation of directive-based performance portable programming models
          Authors: M. Graham Lopez, Wayne Joubert, Veronica Vergara Larrea, Oscar Hernandez, Azzam Haidar, Stanimire Tomov, Jack Dongarra
          Abstract: We present an extended exploration of the performance portability of directives provided by OpenMP 4 and OpenACC to program various types of node architecture with attached accelerators, both self-hosted multicore and offload multicore/GPU. Our goal is to examine how successful OpenACC and the newer offload features of OpenMP 4.5 are for moving codes between architectures, and we document how much tuning might be required and what lessons we can learn from these experiences. To do this, we use examples of algorithms with varying computational intensities for our evaluation, as both compute and data access efficiency are important considerations for overall application performance. To better understand fundamental compute vs. bandwidth bound characteristics, we add the compute-bound Level 3 BLAS GEMM kernel to our linear algebra evaluation. We implement the kernels of interest using various methods provided by newer OpenACC and OpenMP implementations, and we evaluate their performance on various platforms including both x86_64 and Power8 with attached NVIDIA GPUs, x86_64 multicores, self-hosted Intel Xeon Phi KNL, as well as an x86_64 host system with Intel Xeon Phi coprocessors. We update these evaluations with the newest version of the NVIDIA Pascal architecture (P100), Intel KNL 7230, Power8+, and the newest supporting compiler implementations. Furthermore, we present in detail what factors affected the performance portability, including how to pick the right programming model, its programming style, its availability on different platforms, and how well compilers can optimise and target multiple platforms.

          More details...

        • Forthcoming paper


          Title: Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications
          Authors: Robert Searles, Stephen Herbein, Travis Johnston, Michela Taufer, Sunita Chandrasekaran
          Abstract: High performance computing (HPC) offers tremendous potential to process large amounts of data, commonly referred to as big data. Owing to the immense computational requirements of big data applications, the HPC and big data communities are converging. As a result, heterogeneous and distributed systems are becoming commonplace. In order to take advantage of the immense computing power of these systems, distributing data efficiently and leveraging specialised hardware (e.g. accelerators) is critical. In this paper, we develop a portable, high-level paradigm that can be used to run big data applications on existing and future HPC systems. More specifically, we will target graph analytics applications, since these types of application are becoming increasingly popular in the big data and machine learning communities. Using our paradigm, we accelerate three real-world, compute- and data-intensive, graph analytics applications: a function call graph similarity application, a triangle enumeration subroutine, and a graph assaying application. Our paradigm uses the popular MapReduce framework, Apache Spark, in conjunction with CUDA, in order to simultaneously take advantage of automatic data distribution and specialised hardware present on each node of our HPC systems. We demonstrate scalability with regard to compute-intensive portions of the code that are parallelisable, as well as an exploration of the parameter space for each application. We show that our method yields a portable solution that can be used to leverage almost any legacy, current, or next-generation HPC or cloud-based system.

          More details...

        • Forthcoming paper


          Title: Exploring the complete data path for data interoperability in cyber-physical systems
          Authors: Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis, Ilias Maglogiannis, Marinos Themistocleous
          Abstract: The amount of digital information increases tenfold every year, owing to the exponential increase of Cyber-Physical Systems (CPS), real and virtual internet-connected sources. Most researches are focused on data processing and inter-connection fields, leading to the question concerning the interoperable use of data: if data is efficiently processed, how can unknown data be used in a different natures application? A three-stepped approach is presented in this paper, addressing this question, where following the data-lifecycle, a known CPSs dataset is firstly stored into domain-specific language, then translated into domain-agnostic language, and finally, using the fitting function of an ANN, it is compared with an unknown dataset, resulting in the translation of the unknown dataset into the first datasets domain. A scenario of that approach is provided, analysing the data interoperability challenges and needs, emerging from todays Internet of Everything evolution, studying the fields of data annotation, semantics, modelling, and characterisation.

          More details...

        • Forthcoming paper


          Title: Harnessing betweenness centrality for virtual network embedding in tree topologies
          Authors: Mydhili Palagummi, Ricardo Lent
          Abstract: We examine the virtual network embedding problem with QoS constraints and formulate an approach that exploits the betweenness centrality of VNE requests to improve performance. A pay-per-use revenue model is introduced to evaluate the algorithm. An evaluation study using datacentre-like substrates and a wide area topology compares the approach with four embedding methods from the literature and reports on the average revenue rate, embedding success probability, average number of VNE deployments, cost, and impact of substrate failures on the operation of the VNEs, confirming the efficacy of the proposed approach.

          More details...

        • Forthcoming paper


          Title: Monitoring and management of a cloud application within a federation of cloud providers
          Authors: Rocco Aversa, Luca Tasquier
          Abstract: Cloud federation is an emerging computing model where multiple resources from independent cloud providers are leveraged to create large-scale distributed virtual computing clusters, operating as within a single cloud organisation. This concept of service aggregation is characterised by interoperability features, which can address different problems about inter-cloud collaboration, such as vendor lock-in. Furthermore, it approaches challenges like performance and disaster-recovery through methods such as co-location and geographic distribution. One of the main issues within a cloud federation is related to the monitoring of the application deployed on resources coming from different vendors belonging to the federation. In this work we present an agent-based architecture and its prototypal implementation that aims at monitoring the user's cloud environment provided by the federation: the elasticity of the proposed architecture allows the configuration and customisation of the monitoring infrastructure to adapt it to the specific cloud application. A multi-layer architecture is proposed, where each part monitors different aspects of the multi-cloud infrastructure, starting from the detection of critical conditions on low level parameters for the computational units and composing different monitoring levels in order to check the federated SLA. The agent-based approach will introduce fault-tolerance and scalability to the monitoring architecture, while the agents' reactivity and proactivity capabilities will allow a deep and intelligent monitoring, where each agent can focus on different aspects of the monitoring activity, from low level performance indexes to the checking of the federated SLA compliance. Agents will be strengthened by algorithms and rules used to monitor QoS parameters that are critical for the specific application; the configuration of the adaptive monitoring environment will be made easier by an interface that will help the user in describing his/her application's deployment. The prototypal implementation of the proposed framework will be applied on a testbed application to validate the monitoring architecture.

          More details...

        • Forthcoming paper


          Title: Acceleration of unstructured implicit low-order finite-element earthquake simulation using OpenACC on Pascal GPUs
          Authors: Takuma Yamaguchi, Kohei Fujita, Tsuyoshi Ichimura, Muneo Hori, Lalith Maddegedara
          Abstract: We accelerate CPU-based unstructured implicit low-order finite-element simulations by porting to a GPU-CPU heterogeneous compute environment by OpenACC. We modified the algorithm of performance-sensitive parts, such as sparse matrix-vector multiplication and MPI communication, so that computations are suitable for GPUs. Other parts of the earthquake simulation code are ported by directly inserting OpenACC directives into the CPU code. This porting approach enables high performance with relatively low development costs. When comparing eight K computer nodes and eight NVIDIA Pascal P100 GPUs, we achieve 20.8 times speedup for the 3-by-3 block Jacobi preconditioned conjugate gradient finite-element solver. We show the effectiveness of the proposed method through many-case crust-deformation simulations and a large-scale computation using a finite element model with billion degrees-of-freedom on a GPU cluster.

          More details...

        • International Journal of High Performance Computing and Networking increases issues


          The International Journal of High Performance Computing and Networking has announced that it will be increasing issues from six to eight from 2018 onwards.


          13 November 2017
          Denver, Colorado, USA

          Selected authors will be invited to elaborate on their research topic and submit the results to the journal.


          24 - 26 October 2017
          Rabat, Morocco

          Selected authors will be invited to elaborate on their research topic and submit the results to the journal.


          8 - 10 November 2017
          Taichung, Taiwan

          Selected authors will be invited to elaborate on their research topic and submit the results to the journal.