[hpc-announce] HPBDC 2016 Workshop - Deadline Extension and Keynote Speaker Announcement

Xiaoyi Lu@cse.osu luxi at cse.ohio-state.edu
Thu Jan 7 17:09:07 CST 2016


===============================================================

 IEEE International Workshop on High-Performance Big Data Computing (HPBDC)
               http://web.cse.ohio-state.edu/~luxi/hpbdc2016

                    In conjunction with the 30th IEEE
  International Parallel and Distributed Processing Symposium (IPDPS 2016)

                  May 27th, 2016, Chicago, Illinois USA

===============================================================


WORKSHOP DESCRIPTION
---------------------------------------

Managing and processing large volumes of data, or `Big Data', and
gaining meaningful insights is a significant challenge facing the
distributed computing community. This has significant impact on a wide
range of domains including health care, bio-medical research, Internet
search, finance and business informatics, and scientific computing. As
data-gathering technologies and data-sources witness an explosion in
the amount of input data, it is expected that in the future massive
quantities of data in the order of hundreds or thousands of petabytes
will need to be processed. Thus, it is critical that data-intensive
computing middleware (such as Hadoop, HBase and Spark) are diligently
designed, with high performance and scalability, in order to meet the
growing demands of Big Data applications.

The explosive growth of Big Data has caused many industrial firms to
adopt High Performance Computing (HPC) technologies to meet the
requirements of huge amount of data to be processed and stored. Modern
HPC systems and the associated middleware (such as MPI and Parallel
File systems) have been exploiting the advances in HPC technologies
(multi/many-core architectures, accelerators, RDMA-enabled networking,
NVRAMs and SSDs) during the last decade. However, Big Data middleware
(such as Hadoop, HBase and Spark) have not embraced such technologies.
These disparities are taking HPC and Big Data processing into
`divergent trajectories'.

The IEEE International Workshop on High-Performance Big Data Computing
(HPBDC), aims to bring HPC and Big Data processing into a `convergent
trajectory'. The workshop provides a forum for scientists and
engineers in academia and industry to present their latest research
findings on major and emerging topics in this field.

HPBDC 2016 will be held in conjunction with the 30th IEEE
International Parallel and Distributed Processing Symposium (IPDPS
2016), Chicago, Illinois USA, Friday, May 27th, 2016.

HPBDC 2016 welcomes original submissions in a range of areas,
including but not limited to:

 * High-performance Big Data analytics frameworks, programming models,
   and tools

 * Performance optimizations for Big Data systems and applications
   with HPC technologies (multi/many-core architectures, accelerators,
   RDMA-enabled networking, NVRAMs and SSDs)

 * High-performance in-memory computing technologies and abstractions

 * Performance modeling and evaluation for emerging Big Data Computing
   technologies

 * Big Data on HPC, Cloud, and Grid computing infrastructures

 * Fault tolerance, reliability and availability for high-performance
   Big Data Computing

 * Green Big Data Computing

 * Scientific Computing with Big Data

Papers should present original research. As Big Data spans many
disciplines, papers should provide sufficient background material to
make them accessible to the broader community.


KEYNOTE SPEAKER
-----------------------------

Dr. Chaitanya Baru, Senior Advisor for Data Science, National Science
Foundation (NSF); Distinguished Scientist, San Diego Supercomputer
Center (SDSC)
  - Title: TBD


SUBMISSION INFORMATION
-----------------------------------------

All submissions should follow the IEEE standard 8.5x11 two-column
format. The workshop will accept traditional research papers (8-10
pages) for in-depth topics and short papers (4 pages) for works in
progress on hot topics.

 - Long papers: 8-10 pages, with a full problem description,
   background and related work, design, and evaluation.

 - Short papers: 4 pages, for works in progress on hot topics.

All the papers should be submitted through
http://edas.info/newPaper.php?c=21560.

All papers will be carefully reviewed by at least three reviewers.
Papers should not be submitted in parallel to any other conference or
journal.

The proceedings of this workshop will be published together with the
proceedings of other IPDPS 2016 workshops by the IEEE Computer Society
Press. Proceedings of the workshops are distributed at the conference
and are submitted for inclusion in the IEEE Xplore Digital Library
after the conference. At least one of the authors of each accepted
paper must register as a participant of the workshop and present the
paper at the workshop, in order to have the paper published in the
proceedings.


IMPORTANT DATES (Extended)
--------------------------------------------

 - Abstract submission deadline: January 18th, 2016 (Optional, Anywhere
   on Earth)
 - Paper submission deadline: January 25th, 2016 (Anywhere on Earth)
 - Acceptance notification: February 14th, 2016
 - Camera-ready deadline: February 28th, 2016
 - Workshop: May 27th, 2016


WORKSHOP ORGANIZERS
--------------------------------------

Dhabaleswar K. (DK) Panda, The Ohio State University
Jianfeng Zhan, Institute of Computing Technology, Chinese Academy of
               Sciences, China
Xiaoyi Lu, The Ohio State University


PROGRAM COMMITTEE
----------------------------------

Chaitanya Baru, San Diego Supercomputer Center, National Science Foundation
Rajesh Bordawekar, IBM Thomas J. Watson Research Center
Yanpei Chen, Cloudera
Geoffrey Fox, Indiana University Bloomington
Bingsheng He, Nanyang Technological University, Singapore
Lizy John, The University of Texas at Austin
Xu Liu, The College of William and Mary
Raghunath Nambiar, Cisco
Manoj Nambiar, Tata Consultancy Services Ltd., India
Ren Wu, NovuMind
Li Zha, Institute of Computing Technology, Chinese Academy of Sciences, China
Yunquan Zhang, Institute of Computing Technology, Chinese Academy of
               Sciences, China


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