[Paraview] ANNOUNCE HPDAV workshop: Call for papers

[Berk Geveci]

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We invite papers on original, unpublished research in the following topic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
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