[hpc-announce] HPDAV 2016 - Call for papers

[Wes Bethel]

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HPDAV 2016
Call for papers
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The workshop on
High Performance Data Analysis and Visualization (HPDAV) 2016
http://vis.lbl.gov/Events/HPDAV-IPDPS-2016/
May 23, 2016

To be held in conjunction with
30th IEEE International Parallel and Distributed Processing Symposium
http://www.ipdps.org/
May 23-26, 2016
Chicago Hyatt Regency, Chicago, Illinois, USA




Important Dates (AoE)
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Paper Submission: Jan. 10, 2016
Paper Notification: Feb. 7, 2016
Camera-Ready: Feb. 21, 2016



Workshop Scope and Goals
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While the purpose of visualization and analysis is insight, realizing 
that objective requires solving complex problems related to crafting or 
adapting algorithms and applications to take advantage of evolving 
architectures, and to solve increasingly complex data understanding 
problems for ever larger and more complex data. These architectures, and 
the systems from which they are built, have increasingly deep memory 
hierarchies, increasing concurrency, decreasing relative 
per-core/per-node I/O capacity, lessening memory per core, are 
increasingly prone to failures, and face power limitations.

The purpose of this workshop is to bring together researchers, 
engineers, and architects of data-intensive computing technologies, 
which span visualization, analysis, and data management, to present and 
discuss research topics germane to high performance data analysis and 
visualization. Specifically, this workshop focuses on research topics 
related to adapting/creating algorithms, technologies, and applications 
for use on emerging computational architectures and platforms.

The workshop format includes traditional research papers (8-10 pages) 
for in-depth topics, short papers (4 pages) for works in progress, and a 
panel discussion.

Proceedings of the workshops are distributed at the conference and are 
submitted for inclusion in the IEEE Xplore Digital Library after the 
conference.

We invite papers on original, unpublished research in the following 
topic areas under the general umbrella of high performance visualization 
and analysis:

- Increasing concurrency at the node level, and at the systemwide level.
- Optimizations for improving performance, e.g., decreasing runtime, 
leveraging a deepening memory hierarchy, reducing data movement, 
reducing power consumption.
- Applications of visualization and analysis, where there is a strong 
thematic element related to being able to solve a larger or more complex 
problem because of algorithmic or design advances that take advantage of 
increasing concurrency, architectural features, etc.
- Data analysis and/or visualization systems/designs/architectures 
having an emphasis upon scalability, resilience, 
high-throughput/high-capacity, and that are able to take advantage of 
emerging architectures.

Paper submission guidelines: see
http://vis.lbl.gov/Events/HPDAV-IPDPS-2016




Program Committee
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Jeff Baumes, Kitware
Janine Bennett, Sandia National Laboratory
Wes Bethel, Lawrence Berkeley National Laboratory
Randall Frank, Applied Research Associates
Kelly Gaither, Texas Advanced Computing Center
Christoph Garth, University of Kaiserslautern
Berk Geveci, Kitware
Pat McCormick, Los Alamos National Laboratory
Vijay Natarajan, Indian Institute of Science
Paul Navratil, Texas Advanced Computing Center
Sang-Yun Oh, University of California -- Santa Barbara
Rob Ross, Argonne National Laboratory
Yogesh Simmhan, Indian Institute of Science
Venkat Vishwanath, Argonne National Laboratory
Johann Won, Seoul National University
John Wu, Lawrence Berkeley National Laboratory