High-Performance Computing for Big Data Processing

Introduction

High-performance computing has been an important and fundamental research topic over the past decade and has posed many challenging problems. Researchers and industrial professionals have been devoted to designing innovative tools and techniques to keep up with the rapid evolution and increasing complexity of large and complex scientific and engineering problems. Recent years have witnessed a deluge of network data propelled by the vehicular communications, mobile sensing, IoT, M2M communications, emerging online social media, user-generated video contents, and global-scale communications, bringing people into the era of big data. These network data hold much valuable information that could significantly improve the effective and intelligent optimisation of Internet, vehicular networking, mobile networking, and IoT. Big Data processing requires a vast amount of storage and computing resources. In addition, online and robust processing is needed for some circumstances, e.g., life-or-death situations. The high-performance computing techniques have been widely agreed as a promising paradigm to facilitate big data processing, but with tremendous research challenges in recent years, such as the scalability of computing performance for high velocity, high variety, and high volume big data, Deep learning with massive-scale datasets, MapReduce on multi-core, GPU, and hybrid distributed environments, and unstructured data processing with high-performance computing.

This special issue is devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects on high-performance computing techniques for big data processing, and aims to provide worldwide researchers and practitioners an ideal platform to innovate new solutions targeting at the corresponding key challenges.

Topics

Topics of interest include but are not limited to the following:

• Computing performance scalability for high velocity, high variety, and high volume big data
• Unstructured data processing with high-performance computing
• Hadoop, MapReduce, Spark, Storm, Streaming for Big Data processing
• Deep learning with massive-scale datasets
• Heterogeneous computing for Big Data architecture
• Processing large-scale datasets using high-performance computing
• Querying and visualization of large network datasets
• Convergence and complexity issues, and performance analysis for big data processing using high-performance computing
• Online, active, decentralized big data processing
• MapReduce on multi-core, GPU, and hybrid distributed environments
• High-performance computing in systems biology, genomics, bioinformatics, health, medical, semantics, sentiment and natural language processing
• Green energy and smart power grid analytics; climate, astronomical, geoscience analytics using high-performance computing

Important Date

Paper Submission Due (First Round): July 31, 2017

First-Round Peer-review:  September 30, 2017

Author Revisions: October 31, 2017

Second-Round Peer-review: November 15, 2017

Final Version Submissions: November 30, 2017

Tentative Publication Date: Early 2018

Submission Guidelines

Original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are sought. Papers will be peer-reviewed by independent reviewers and selected based on originality, scientific quality and relevance to this Special Issue. The journal editors will make final decisions about the acceptance of the papers.

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generation Computer Systems at http://ees.elsevier.com/fgcs/. Authors should select “VSI: HPC for Big Data” when they reach the “Article Type” step in the submission process.

Guest Editors

Dr. Yulei Wu

University of Exeter, UK

Email: [email protected]

Professor Yang Xiang

Deakin University, Australia

Email: [email protected]

Professor Jingguo Ge

Chinese Academy of Sciences, China

Email: [email protected]

Dr. Peter Muller

IBM Zurich Research Laboratory, Switzerland

Email: [email protected]