[hpc-announce] CfP: Special Issue of JPDC on "Data Intensive Computing", Submission: Jan 15th 2010

Suren Byna sbyna at nec-labs.com
Wed Sep 16 05:30:54 CDT 2009


Call for Papers:

Special Issue of Journal of Parallel and Distributed Computing on  
"Data Intensive Computing"
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Data intensive computing is posing many challenges in exploiting  
parallelism of current and upcoming computer architectures. Data  
volumes of applications in the fields of sciences and engineering,  
finance, media, online information resources, etc. are expected to  
double every two years over the next decade and further. With this  
continuing data explosion, it is necessary to store and process data  
efficiently by utilizing enormous computing power that is available in  
the form of multi/manycore platforms. There is no doubt in the  
industry and research community that the importance of data intensive  
computing has been raising and will continue to be the foremost fields  
of research. This raise brings up many research issues, in forms of  
capturing and accessing data effectively and fast, processing it while  
still achieving high performance and high throughput, and storing it  
efficiently for future use. Programming for high performance yielding  
data intensive computing is an important challenging issue. Expressing  
data access requirements of applications and designing programming  
language abstractions to exploit parallelism are at immediate need.  
Application and domain specific optimizations are also parts of a  
viable solution in data intensive computing. While these are a few  
examples of issues, research in data intensive computing has become  
quite intense during the last few years yielding strong results.

This special issue of the Journal Parallel and Distributed Computing  
(JPDC) is seeking original unpublished research articles that describe  
recent advances and efforts in the design and development of data  
intensive computing, functionalities and capabilities that will  
benefit many applications.

Topics of interest include (but are not limited to):

*  Data-intensive applications and their challenges
*  Storage and file systems
*  High performance data access toolkits
*  Fault tolerance, reliability, and availability
*  Meta-data management
*  Remote data access
*  Programming models, abstractions for data intensive computing
*  Compiler and runtime support
*  Data capturing, management, and scheduling techniques
*  Future research challenges of data intensive computing
*  Performance optimization techniques
*  Replication, archiving, preservation strategies
*  Real-time data intensive computing
*  Network support for data intensive computing
*  Challenges and solutions in the era of multi/many-core platforms
*  Stream computing
*  Green (Power efficient) data intensive computing
*  Security and protection of sensitive data in collaborative  
environments

Guide for Authors

Papers need not be solely abstract or conceptual in nature: proofs and  
experimental results can be included as appropriate.

Authors should follow the JPDC manuscript format as described in the  
"Information for Authors" at the end of each issue of JPDC or at http://ees.elsevier.com/jpdc/ 
  . The journal version will be reviewed as per JPDC review process  
for special issues.

Important Dates:

Paper Submission			:  January 15, 2010
Notification of Acceptance/Rejection   	:  May 31, 2010
Final Version of the Paper           	:  September 15, 2010


Submission Guidelines

All manuscripts and any supplementary material should be submitted  
through Elsevier Editorial System (EES) at http://ees.elsevier.com/ 
jpdc . Authors must select "Special Issue: Data Intensive Computing"  
when they reach the "Article Type" step in the submission process.  
First time users must register themselves as Author. For the latest  
details of the JPDC special issue see http://www.cs.iit.edu/~suren/jpdc

Guest Editors:
Dr. Surendra Byna
NEC Labs America
E-mail: sbyna at nec-labs.com

Prof. Xian-He Sun
Illinois Institute of Technology
E-mail: sun at cs.iit.edu



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