[IPDPS] Call for Participation GrAPL 2020 - LIVE Q&A Session 18 May 2020 (8 AM PDT)
Bader, David
bader at njit.edu
Fri May 15 15:22:49 UTC 2020
CALL FOR PARTICIPATION
*******************************************************************************
GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning
https://hpc.pnl.gov/grapl/
May 18, 2020
8AM – 10AM PDT
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 (http://www.ipdps.org) 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 (https://hpc.pnl.gov/grapl/) by
May 15th.
*To attend the Zoom Sessions, we ask participants to register in advance at
the following link: https://tinyurl.com/grapl2020
<https://tinyurl.com/grapl2020>*
The organizing committee will then provide the link to the session.
******************************************************************************
Program for May 18th:
0800 – 0845 (PDT): Session 1
Welcome message.
*Algorithms and Applications*
Kronecker Graph Generation with Ground Truth for 4-Cycles and Dense
Structure in Bipartite Graphs
*Trevor Steil (University of Minnesota), Scott McMillan (SEI, Carnegie
Mellon University), Geoffrey Sanders (LLNL), Roger Pearce (LLNL), Benjamin
Priest (LLNL)*
A scalable graph generation algorithm to sample over a given shell
distribution
*M. Yusuf Özkaya (Georgia Institute of Technology), Muhammed Fatih Balin
(Georgia Institute of Technology), Ali Pinar (SNL), Ümit V. Çatalyürek
(Georgia Institute of Technology)*
An incremental GraphBLAS solution for the 2018 TTC Social Media case study
*Márton Elekes (Budapest University of Technology and Economics), Gábor
Szárnyas (Budapest University of Technology and Economics)*
Linear Algebraic Louvain Method in Python
*Tze Meng Low (Carnegie Mellon University), Daniele Spampinato (Carnegie
Mellon University), Scott McMillan (SEI, Carnegie Mellon University),
Michel Pelletier (FPX, LLC)*
0900 – 0945 (PDT): Session 2
Keynote - The GraphIt Universal Graph Framework: Achieving High-Performance
across Algorithms, Graph Types and Architectures
*Saman Amarasinghe (Massachusetts Institute of Technology)*
*API's and Implementations*
Parallelizing Maximal Clique Enumeration on Modern Manycore Processors
*Jovan Blanuša (IBM Research - Zürich, EPFL), Radu Stoica (IBM Research -
Zürich), Paolo Ienne (EPFL), Kubilay Atasu (IBM Research - Zürich)*
A Roadmap for the GraphBLAS C++ API
* Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson
(Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira
(IBM)*
Considerations for a Distributed GraphBLAS API
* 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)*
75,000,000,000 Streaming Inserts/Second Using Hierarchical Hypersparse
GraphBLAS Matrices
* Jeremy Kepner (MIT Lincoln Laboratory)*
******************************************************************************
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
SUMMARY
-------
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.
ORGANIZATION
------------
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)
[image: NJIT logo] <https://www.njit.edu/> *David A. Bader*
Distinguished Professor
Computer Science
david.bader at njit.edu • (973) 596-2654 <(973)+596-2654>
A Top 100 National University
*U.S. News & World Report*
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