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Call for Papers</div>
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Fifth International Workshop on Visual Performance Analysis (VPA18)</div>
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Held in conjunction with SC18:</div>
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The International Conference on High Performance Computing,</div>
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Networking, Storage and Analysis</div>
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Dallas, TX, USA</div>
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<span><span>November 11, 2018</span></span> (<span><span>Sunday</span></span>)</div>
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<span> <a href="https://vpa18.github.io" target="_blank">https://vpa18.github.io</a><br>
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Submission Deadline: <span><span>August 6, 2018</span></span></div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">Over the last decades an incredible amount of resources has been devoted to</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">building ever more powerful supercomputers. However, exploiting the full</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">capabilities of these machines is becoming exponentially more difficult with</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">each new generation of hardware. To help understand and optimize the behavior</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">of massively parallel simulations the performance analysis community has</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">created a wide range of tools and APIs to collect performance data, such as</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">flop counts, network traffic or cache behavior at the largest scale. However,</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">this success has created a new challenge, as the resulting data is far too</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">large and too complex to be analyzed in a straightforward manner. Therefore,</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">new automatic analysis and visualization approaches must be developed to allow</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">application developers to intuitively understand the multiple, interdependent</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">effects that their algorithmic choices have on the final performance.</font></div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">This workshop will bring together researchers from the fields of performance</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">analysis and visualization to discuss new approaches of applying visualization</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">and visual analytics techniques to large scale applications.</font></div>
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Workshop Topics:</div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Scalable displays of performance data</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Case studies demonstrating the use of performance visualization in practice</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Data models to enable scalable visualization</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Graph representation of unstructured performance data</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Presentation of high-dimensional data</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Visual correlations between multiple data source</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Human-Computer Interfaces for exploring performance data</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">- Multi-scale representations of performance data for visual exploration</font></div>
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Paper Submission:</div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">We solicit 8-page full papers as well as 4-page short papers or position papers</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">that focus on </font><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif">techniques OR case studies at the intersection of performance analysis</span></div>
<div><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif">and </span><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif">visualization, and either use visualization techniques
to display large scale</span></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">performance data or that develop new visualization or visual analytics methods</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">that help create new insights.</font></div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">Papers must be submitted in PDF format (readable by Adobe Acrobat Reader 5.0</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">and higher) and formatted for 8.5" x 11" (U.S. Letter). Submissions are limited</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">to 8 pages in the IEEE</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">format (<a href="https://www.ieee.org/conferences/publishing/templates.html" target="_blank">https://www.ieee.org/conferen<wbr>ces/publishing/templates.html</a>)<wbr>, using the</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">sample-sigconf template. The 8-page limit includes figures, tables, and</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">references.</font></div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">All papers must be submitted through the Supercomputing 2018 Linklings site:</font></div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222"><a href="http://submissions.supercomputing.org" target="_blank">http://submissions.supercomput<wbr>ing.org</a></font></div>
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Important Dates:</div>
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* Submission deadline: <span><span>August 6, 2018</span></span>. (AoE)</div>
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* Notification of acceptance: <span><span>September 17, 2018</span></span> (AoE)</div>
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* Camera-ready deadline: <span><span>October 8, 2018</span></span> (AoE)</div>
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Workshop Chairs:</div>
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<div><font face="Arial, Helvetica, sans-serif" color="#222222">Abhinav Bhatele, Lawrence Livermore National Laboratory</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">Kevin Huck, University of Oregon</font></div>
<div><font face="Arial, Helvetica, sans-serif" color="#222222">Kate Isaacs, University of Arizona</font></div></div><br></div>