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CALL FOR PAPERS
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GrAPL 2020: Workshop on Graphs, Architectures, Programming, and
Learning
https://hpc.pnl.gov/grapl/
May 18, 2020
Co-Located with IPDPS 2020
New Orleans
Louisiana, USA
GrAPL is the result of the combination of two IPDPS workshops:
GABB: Graph Algorithms Building Blocks
GraML: Workshop on The Intersection of Graph Algorithms and
Machine Learning
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SUMMARY
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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.
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:
• 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;
• Discuss the problem domains and problems addressable with graph
methods, machine learning methods, or both;
• Discuss programming models and associated frameworks such as
Pregel, Galois, Boost, GraphBLAS, GraphChi, etc., for building
large multi-attributed graphs;
• Discuss how frameworks for building graph algorithms interact
with those for building machine learning algorithms;
• Discuss hardware platforms specialized for addressing large,
dynamic, multi-attributed graphs and associated machine learning;
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.
IMPORTANT DATES
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Position or full paper submission: February 3, 2020
Author Notification: February 29, 2020
Camera-ready: March 15, 2020
Workshop: May 18, 20120
PAPER SUBMISSIONS
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Submissions will be done through Linklings:
https://ssl.linklings.net/conferences/ipdps/
Please visit GrAPL'20 website for instructions:
https://hpc.pnl.gov/grapl/
Authors can submit two types of papers: Short papers (up to 4
pages) and long papers (up to 10 pages). All submissions must be
single-spaced double-column pages using 10-point size font on
8.5x11 inch pages (IEEE conference style), including figures,
tables, and references.
The templates are available at:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
ORGANIZATION
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General co-Chairs:
Scott McMillan (CMU SEI),
smcmillan@sei.cmu.edu
Manoj Kumar (IBM),
manoj1@us.ibm.com
Program Chairs:
Danai Koutra (University of Michigan, Ann Arbor),
dkoutra@umich.edu
Mahantesh Halappanavar (PNNL),
hala@pnnl.gov
GrAPL's Little Helpers:
Tim Mattson (Intel)
Antonino Tumeo (PNNL)
Program Committee:
Nesreen K Ahmed, Intel Research and Intel AI, USA
Sasikanth Avancha, Intel Labs - Parallel Computing Lab, India
Aydin Buluç, Lawrence Berkeley National Lab, USA
Timothy A. Davis, University of Florida, USA
Jana Doppa, Washington State University, USA
John Gilbert, University of California at Santa Barbara, USA
Sergio Gómez, Universitat Rovira i Virgili, Catalonia
Will Hamilton, McGill University, Mila, Canada
Stratis Ioannidis, Northeastern University, Boston, USA
Bharat Kaul, Intel Labs - Parallel Computing Labs, India
Kamesh Madduri, The Pennsylvania State University, USA
Henning Meyerhenke, Humboldt University of Berlin, Germany
Indranil Roy, Natural Intelligence, USA
Robert Rallo, Pacific Northwest National Lab, USA
P. Sadayappan, University of Utah, USA
Yizhou Sun, University of California, Los Angeles, USA
Flavio Vella, Free University of Bozen, Italy
Steering Committee:
David A. Bader (New Jersey Institute of Technology)
Aydın Buluç (LBNL)
John Feo (PNNL)
John Gilbert (UC Santa Barbara)
Tim Mattson (Intel)
Ananth Kalyanaraman (Washington State University)
Jeremy Kepner (MIT Lincoln Laboratory)
Antonino Tumeo (PNNL)