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<p class="MsoNormal">CALL FOR PARTICIPATION<br>
<br>
*******************************************************************************<br>
<br>
GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning (at IEEE IPDPS)<br>
<a href="https://hpc.pnl.gov/grapl/">https://hpc.pnl.gov/grapl/</a><br>
<br>
May 18, 2020<o:p></o:p></p>
<p class="MsoNormal">8AM – 10AM PDT<o:p></o:p></p>
<p class="MsoNormal"><br>
IMPORTANT: This year, GrAPL will hold two LIVE 45 minute Q&A sessions with the authors of the accepted papers and invited talks according to the current schedule below. Papers and static presentations for the entire conference including the GrAPL Workshop
will be made available to all conference registrants by Friday May 15th. Register for free at the IPDPS website (<a href="http://www.ipdps.org">http://www.ipdps.org</a>) to get instructions on how to access to this content. In addition, links to 3-5 minute
lightning talks by the workshop speakers will be found at the GrAPL website (<a href="https://hpc.pnl.gov/grapl/">https://hpc.pnl.gov/grapl/</a>) by May 15<sup>th</sup>.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p style="margin:0in;margin-bottom:.0001pt"><strong><span style="font-family:"Calibri",sans-serif">To attend the Zoom Sessions, we ask participants to register in advance at the following link:
<a href="https://tinyurl.com/grapl2020">https://tinyurl.com/grapl2020</a><o:p></o:p></span></strong></p>
<p style="margin:0in;margin-bottom:.0001pt"><o:p> </o:p></p>
<p style="margin:0in;margin-bottom:.0001pt">The organizing committee will then provide the link to the session.<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:12.0pt"><br>
******************************************************************************<o:p></o:p></p>
<p class="MsoNormal">Program for May 18<sup>th</sup>:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">0800 – 0845 (PDT): Session 1<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:1.0in">Welcome message.<em><span style="font-family:"Calibri",sans-serif"><o:p></o:p></span></em></p>
<p class="MsoNormal" style="margin-left:1.0in"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:1.0in"><b>Algorithms and Applications<o:p></o:p></b></p>
<p class="MsoNormal" style="margin-left:1.0in"><b><o:p> </o:p></b></p>
<p class="MsoNormal" style="margin-left:1.0in">Kronecker Graph Generation with Ground Truth for 4-Cycles and Dense Structure in Bipartite Graphs<br>
<em><span style="font-family:"Calibri",sans-serif">Trevor Steil (University of Minnesota), Scott McMillan (SEI, Carnegie Mellon University), Geoffrey Sanders (LLNL), Roger Pearce (LLNL), Benjamin Priest (LLNL)</span></em><br>
<br>
A scalable graph generation algorithm to sample over a given shell distribution<br>
<em><span style="font-family:"Calibri",sans-serif">M. Yusuf Özkaya (Georgia Institute of Technology), Muhammed Fatih Balin (Georgia Institute of Technology), Ali Pinar (SNL), Ümit V. Çatalyürek (Georgia Institute of Technology)</span></em><br>
<br>
An incremental GraphBLAS solution for the 2018 TTC Social Media case study<br>
<em><span style="font-family:"Calibri",sans-serif">Márton Elekes (Budapest University of Technology and Economics), Gábor Szárnyas (Budapest University of Technology and Economics)</span></em><br>
<br>
Linear Algebraic Louvain Method in Python<br>
<em><span style="font-family:"Calibri",sans-serif">Tze Meng Low (Carnegie Mellon University), Daniele Spampinato (Carnegie Mellon University), Scott McMillan (SEI, Carnegie Mellon University), Michel Pelletier (FPX, LLC)</span></em><b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">0900 – 0945 (PDT): Session 2<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:1.0in">Keynote - The GraphIt Universal Graph Framework: Achieving High-Performance across Algorithms, Graph Types and Architectures<br>
<em><span style="font-family:"Calibri",sans-serif">Saman Amarasinghe (Massachusetts Institute of Technology)</span></em><br>
<br>
<strong><span style="font-family:"Calibri",sans-serif">API's and Implementations</span></strong><br>
<em><span style="font-family:"Calibri",sans-serif"> </span></em><br>
Parallelizing Maximal Clique Enumeration on Modern Manycore Processors<br>
<em><span style="font-family:"Calibri",sans-serif">Jovan Blanuša (IBM Research - Zürich, EPFL), Radu Stoica (IBM Research - Zürich), Paolo Ienne (EPFL), Kubilay Atasu (IBM Research - Zürich)</span></em><i><br>
</i><br>
<em><span style="font-family:"Calibri",sans-serif"> </span></em>A Roadmap for the GraphBLAS C++ API<br>
<em><span style="font-family:"Calibri",sans-serif"> Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM)</span></em><br>
<em><span style="font-family:"Calibri",sans-serif"> </span></em><br>
Considerations for a Distributed GraphBLAS API<br>
<em><span style="font-family:"Calibri",sans-serif"> Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM), Roger Pearce (LLNL), Oguz Selvitopi (LBNL), Trevor
Steil (University of Minnesota)</span></em><br>
<br>
75,000,000,000 Streaming Inserts/Second Using Hierarchical Hypersparse GraphBLAS Matrices<br>
<em><span style="font-family:"Calibri",sans-serif"> Jeremy Kepner (MIT Lincoln Laboratory)<o:p></o:p></span></em></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-bottom:12.0pt">******************************************************************************<o:p></o:p></p>
<p class="MsoNormal">GrAPL is the result of the combination of two IPDPS workshops:<br>
GABB: Graph Algorithms Building Blocks<br>
GraML: Workshop on The Intersection of Graph Algorithms and Machine Learning<br>
<br>
SUMMARY<br>
-------<br>
<br>
Data analytics is one of the fastest growing segments of computer science. Many real-world analytic workloads are a mix of graph and machine learning methods. Graphs play an important role in the synthesis and analysis of relationships and organizational structures,
furthering the ability of machine-learning methods to identify signature features. Given the difference in the parallel execution models of graph algorithms and machine learning methods, current tools, runtime systems, and architectures do not deliver consistently
good performance across data analysis workflows. In this workshop we are interested in graphs, how their synthesis (representation) and analysis is supported in hardware and software, and the ways graph algorithms interact with machine learning. The workshop’s
scope is broad and encompasses the wide range of methods used in large-scale data analytics workflows.<br>
<br>
This workshop seeks papers on the theory, model-based analysis, simulation, and analysis of operational data for graph analytics and related machine learning applications. In particular, we are interested, but not limited to the following topics:<br>
<br>
• Provide tractability and performance analysis in terms of complexity, time-to-solution, problem size, and quality of solution for systems that deal with mixed data analytics workflows;<br>
• Discuss the problem domains and problems addressable with graph methods, machine learning methods, or both;<br>
• Discuss programming models and associated frameworks such as Pregel, Galois, Boost, GraphBLAS, GraphChi, etc., for building large multi-attributed graphs;<br>
• Discuss how frameworks for building graph algorithms interact with those for building machine learning algorithms;<br>
• Discuss hardware platforms specialized for addressing large, dynamic, multi-attributed graphs and associated machine learning;<br>
<br>
Besides regular papers, short papers (up to four pages) describing work-in-progress or incomplete but sound, innovative ideas related to the workshop theme are also encouraged.<br>
<br>
ORGANIZATION<br>
------------<br>
<br>
General co-Chairs:<br>
<br>
Scott McMillan (CMU SEI), <a href="mailto:smcmillan@sei.cmu.edu">smcmillan@sei.cmu.edu</a><br>
Manoj Kumar (IBM), <a href="mailto:manoj1@us.ibm.com">manoj1@us.ibm.com</a><br>
<br>
Program Chairs:<br>
<br>
Danai Koutra (University of Michigan, Ann Arbor), <a href="mailto:dkoutra@umich.edu">
dkoutra@umich.edu</a><br>
Mahantesh Halappanavar (PNNL), <a href="mailto:hala@pnnl.gov">hala@pnnl.gov</a><br>
<br>
GrAPL's Little Helpers:<br>
<br>
Tim Mattson (Intel)<br>
Antonino Tumeo (PNNL)<br>
<br>
Program Committee:<br>
<br>
Nesreen K Ahmed, Intel Research and Intel AI, USA<br>
Sasikanth Avancha, Intel Labs - Parallel Computing Lab, India<br>
Aydin Buluç, Lawrence Berkeley National Lab, USA<br>
Timothy A. Davis, University of Florida, USA<br>
Jana Doppa, Washington State University, USA<br>
John Gilbert, University of California at Santa Barbara, USA<br>
Sergio Gómez, Universitat Rovira i Virgili, Catalonia<br>
Will Hamilton, McGill University, Mila, Canada<br>
Stratis Ioannidis, Northeastern University, Boston, USA<br>
Bharat Kaul, Intel Labs - Parallel Computing Labs, India<br>
Kamesh Madduri, The Pennsylvania State University, USA<br>
Henning Meyerhenke, Humboldt University of Berlin, Germany<br>
Indranil Roy, Natural Intelligence, USA<br>
Robert Rallo, Pacific Northwest National Lab, USA<br>
P. Sadayappan, University of Utah, USA<br>
Yizhou Sun, University of California, Los Angeles, USA<br>
Flavio Vella, Free University of Bozen, Italy<br>
<br>
Steering Committee:<br>
<br>
David A. Bader (New Jersey Institute of Technology)<br>
Aydın Buluç (LBNL)<br>
John Feo (PNNL)<br>
John Gilbert (UC Santa Barbara)<br>
Tim Mattson (Intel)<br>
Ananth Kalyanaraman (Washington State University)<br>
Jeremy Kepner (MIT Lincoln Laboratory)<br>
Antonino Tumeo (PNNL)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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