[hpc-announce] [CFP] GrAPL 2020 - Deadline Extended to February 10

Tumeo, Antonino Antonino.Tumeo at pnnl.gov
Sat Feb 1 12:03:36 CST 2020

[Please accept our apologies for multiple postings.]

GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning
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
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.
Position or full paper submission: February 10, 2020 (EXTENDED)
Author Notification: February 29, 2020
Camera-ready: March 15, 2020
Workshop: May 18, 20120
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:
General co-Chairs:
    Scott McMillan (CMU SEI), smcmillan at sei.cmu.edu
    Manoj Kumar (IBM), manoj1 at us.ibm.com
Program Chairs:
    Danai Koutra (University of Michigan, Ann Arbor), dkoutra at umich.edu
    Mahantesh Halappanavar (PNNL), hala at 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)

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