[hpc-announce] CFP:Special Issue for Journal of Big Data Research: Tutorials of HPC Tools and Methods for Big Data

[Weijia Xu]

CFP: Special Issue, Journal of Big Data Research: Tutorials of HPC Tools  and Methods for Big Data

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Special Issue on Tutorials of High Performance Computing Tools and Methods for Big Data
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http://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-tutorials-of-high-performance-computing/

Driven by the big data analytics needs, new computing and storage technologies are developing rapidly and pushing for new high-end hardware geared toward big data problems. While the high performance computing technologies have the potential to greatly improve effectiveness of big data analytics, the cost and sophistications of those technology and limited initial application support often make them inaccessible to the end users and not fully utilized in academia years later. Meanwhile, comprehensive analytic software environment and platforms, such as R and Python, have become increasingly popular open-source platforms for data analysis. The software not only provides collection of analytic methods but also has the potential to utilize new hardware transparently and ease the efforts required from the end users. However, most data scientists have only had experience with small to medium-sized data; and now the size of Big Data poses its own challenges. It is therefore a critical issue to make the latest technology advancements in software and hardware accessible to the domain scientists in a timely manner. We are looking for contributions to a special issue for Journal of Big Data Research that present tutorials of high performance computing tools and methods for big data problem. About the Topics of InterestThe topics of interest include but are not limited to: - Adopting hardware technology, such as GPGPU, Xeon Phi etc, for Big Data analytics - Libraries and programming languages environment utilizing HPC for Big Data analytics - Novel software platforms and models for big data collection management - Service oriented architectures to enable data science - Software and platforms for big data analysis and visualization - Submission Format and Guideline All submitted papers should contain only original work, which has not been previously published nor is currently under review by any other journals or conferences. Clearly written in excellent English, papers must not exceed 25 pages (one-column, at least 11pt fonts) including figures, tables, and references. A detailed submission guideline is available as “Guide to Authors” at: http://www.journals.elsevier.com/big-data-research All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “Article Type” when they reach the “Article Type” step in the submission process. All papers will be peer-reviewed by three independent reviewers. Requests for additional information should be addressed to the guest editors (xwj at tacc.utexas.edu<mailto:xwj at tacc.utexas.edu>) Editors in Chief B.C. Ooi Z. Wu Guest Editors Weijia Xu Tony Xiaohua Hu Victor Eijkhout Important dates Submission deadline: March 31 2016 Acceptance deadline: July 31 2016 Publication: October 1 2016
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