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Call for papers: PDSW’23<br>
The 8th International Parallel Data Systems
Workshop<br>
<a href="http://www.pdsw.org/"
target="_blank" class="moz-txt-link-freetext">http://www.pdsw.org</a><br>
November 12, 2023 1:30 PM - 5:00 PM<br>
Held in conjunction with SC23, Denver, CO<br>
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</font><br>
<font face="monospace"><font face="arial, sans-serif">Important
Dates<br>
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face="arial, sans-serif"><br>
<br>
<b>Work in Progress (WIP)</b> <b>submissions due: Sept
15th, 2023, 11:59PM AoE</b><br>
WIP Notification: On or before Sept 23nd, 2023 <br>
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<div class="moz-text-html" lang="x-unicode"><font
face="arial, sans-serif">WIP Session<br>
--------------------------------------------------<br>
<br>
There will be a WIP session where presenters provide brief
5-minute talks on their on-going work, with fresh
problems/solutions. WIP content is typically material that may
not be mature or complete enough for a full paper submission
and will not be included in the proceedings. A one-page
abstract is required. <br>
Submission site: <a class="moz-txt-link-freetext"
href="https://submissions.supercomputing.org/">https://submissions.supercomputing.org/</a></font><br>
<font face="monospace"><font face="arial, sans-serif"> </font><br>
<br>
</font><font face="arial, sans-serif">Abstract</font></div>
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<font face="arial, sans-serif"><font face="monospace"><font
face="arial, sans-serif">----------------------</font></font><font
face="monospace"><font face="arial, sans-serif"><br>
</font></font>We are pleased to announce the 8th
International Parallel Data Systems Workshop (PDSW’23).
PDSW'23 will be hosted in conjunction with SC23: The
International Conference for High Performance Computing,
Networking, Storage and Analysis, in Denver, CO.<br>
<br>
Efficient data storage and data management are crucial to
scientific productivity in both traditional
simulation-oriented HPC environments and Big Data analysis
environments. This issue is further exacerbated by the growing
volume of experimental and observational data, the widening
gap between the performance of computational hardware and
storage hardware, and the emergence of new data-driven
algorithms in machine learning. The goal of this workshop is
to facilitate research that addresses the most critical
challenges in scientific data storage and data processing.
PDSW will continue to build on the successful tradition
established by its predecessor workshops: the Petascale Data
Storage Workshop (PDSW, 2006-2015) and the Data Intensive
Scalable Computing Systems (DISCS 2012-2015) workshop. These
workshops were successfully combined in 2016, and the
resulting joint workshop has attracted up to 38 full paper
submissions and 140 attendees per year from 2016 to 2022. <br>
</font>
<p><font face="arial, sans-serif">We encourage the community to
submit original manuscripts that:</font></p>
<ul>
<li><font face="arial, sans-serif">introduce and evaluate
novel algorithms or architectures,</font></li>
<li><font face="arial, sans-serif">inform the community of
important scientific case studies or workloads, or</font></li>
<li><font face="arial, sans-serif">validate the
reproducibility of previously published work<br>
<br>
</font></li>
</ul>
<font face="arial, sans-serif">Special attention will be given
to issues in which community collaboration is crucial for
problem identification, workload capture, solution
interoperability, standardization, and shared tools. We also
strongly encourage papers to share complete experimental
environment information (software version numbers, benchmark
configurations, etc.) to facilitate collaboration. <br>
</font>
<p><font face="arial, sans-serif">Topics of interest include the
following: <br>
</font></p>
<ul>
<li><font face="arial, sans-serif"> Large-scale data caching
architectures</font></li>
<li><font face="arial, sans-serif"> Scalable architectures for
distributed data storage, archival, and virtualization </font></li>
<li><font face="arial, sans-serif"> The application of new
data processing models and algorithms towards computing
and analysis </font></li>
<li><font face="arial, sans-serif"> Performance benchmarking,
resource management, and workload studies</font></li>
<li><font face="arial, sans-serif"> Enabling cloud and
container-based models for scientific data analysis </font></li>
<li><font face="arial, sans-serif"> Techniques for data
integrity, availability, reliability, and fault tolerance
</font></li>
<li><font face="arial, sans-serif"> Programming models and big
data frameworks for data intensive computing</font></li>
<li><font face="arial, sans-serif"> Hybrid cloud/on-premise
data processing</font></li>
<li><font face="arial, sans-serif"> Cloud-specific data
storage and transit costs and opportunities </font></li>
<li><font face="arial, sans-serif"> Programmability of storage
systems </font></li>
<li><font face="arial, sans-serif"> Data filtering,
compression, reduction techniques </font></li>
<li><font face="arial, sans-serif"> Data and metadata indexing
and querying</font></li>
<li><font face="arial, sans-serif"> Parallel file systems,
metadata management, and complex data management</font></li>
<li><font face="arial, sans-serif"> Integrating computation
into the memory and storage hierarchy to facilitate
in-situ and in-transit data processing </font></li>
<li><font face="arial, sans-serif"> Alternative data storage
models, including object stores and key-value stores </font></li>
<li><font face="arial, sans-serif"> Productivity tools for
data intensive computing, data mining, and knowledge
discovery</font></li>
<li><font face="arial, sans-serif"> Tools and techniques for
managing data movement among compute and data intensive
components </font></li>
<li><font face="arial, sans-serif"> Cross-cloud data
management </font></li>
<li><font face="arial, sans-serif"> Storage system
optimization and data analytics with machine learning</font></li>
<li><font face="arial, sans-serif"> Innovative techniques and
performance evaluation for new memory and storage systems</font></li>
</ul>
<font face="arial, sans-serif"><br>
</font><font face="arial, sans-serif">Regular Paper Submissions<br>
--------------------------------------<br>
<br>
All papers will be evaluated by a competitive peer review
process under the supervision of the workshop program
committee. Selected papers and associated talk slides will be
made available on the workshop web site. The papers will also
be published in the SC23 Workshop Proceedings. <br>
<br>
Authors of regular papers are strongly encouraged to submit
Artifact Description (AD) Appendices that can help to
reproduce and validate their experimental results. While the
inclusion of the AD Appendices is optional for PDSW’23,
submissions that are accompanied by AD Appendices will be
given favorable consideration for the PDSW Best Paper award. <br>
<br>
PDSW’23 follows the SC23 Reproducibility Initiative. For
Artifact Description (AD) Appendices, we will use the format
of the SC23 for PDSW'23 submissions. The AD should include a
field for one or more links to data (zenodo, figshare, etc.)
and code (github, gitlab, bitbucket, etc.) repositories. For
the Artifacts that will be placed in the code repository, we
encourage authors to follow the PDSW 2023 Reproducibility
Addendum on how to structure the artifact, as it will make it
easier for the reviewing committee and readers of the paper in
the future.<br>
<br>
Submit a not previously published paper as a PDF file,
indicate authors and affiliations. Papers must be up to 6
pages, not less than 10 point font and not including
references and optional reproducibility appendices. <br>
<b>Submission site</b>: <a class="moz-txt-link-freetext"
href="https://submissions.supercomputing.org/">https://submissions.supercomputing.org/</a><br>
<br>
<b>Submissions due: </b>July 30th, 2023, 11:59 PM AoE<br>
Papers must use the ACM conference paper template available
at: <br>
<a class="moz-txt-link-freetext"
href="https://www.acm.org/publications/proceedings-template">https://www.acm.org/publications/proceedings-template</a><br>
</font> <font face="arial, sans-serif"> <br>
<br>
Workshop Organizers<br>
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General Chair<br>
</font><font face="arial, sans-serif"><font
face="arial,
sans-serif">- Amelie Chi Zhou, Shenzhen University, China <br>
</font></font><font face="arial, sans-serif"><br>
Program Co-Chairs<br>
- Bing Xie, Oak Ridge National Laboratory, USA <br>
- Suren Byna, The Ohio State University, USA<br>
<br>
Reproducibility Co-Chairs<br>
- Tanu Malik, DePaul University, USA<br>
</font><font face="arial, sans-serif">- Jean Luca Bez, Lawrence
Berkeley National Laboratory, USA <br>
<br>
Publicity Chair<br>
- Kira Duwe, EPFL, Switzerland<br>
<br>
Web and Proceedings Chair<br>
- Joan Digney, Carnegie Mellon University</font> </div>
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