[hpc-announce] GrAPL 2020 - Virtual Event - Call for Participation

Tumeo, Antonino Antonino.Tumeo at pnnl.gov
Thu May 14 22:23:14 CDT 2020

+AFs-Please accept our apologies for multiple postings.+AF0-



GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning

May 18, 2020

IMPORTANT:  This year, GrAPL will hold two LIVE 45 minute Q+ACY-A sessions with the authors of the accepted papers and invited talks according to the 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
The organizing committee will then provide the link to the session.


Program for May 18th:
0800 +IBM- 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 +ANY-zkaya (Georgia Institute of Technology), Muhammed Fatih Balin (Georgia Institute of Technology), Ali Pinar (SNL),  +ANw-mit V. +AMc-ataly+APw-rek (Georgia Institute of Technology)

An incremental GraphBLAS solution for the 2018 TTC Social Media case study
M+AOE-rton Elekes (Budapest University of Technology and Economics), G+AOE-bor Sz+AOE-rnyas (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 +IBM- 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 Blanu+AWE-a (IBM Research - Z+APw-rich, EPFL), Radu Stoica (IBM Research - Z+APw-rich), Paolo Ienne (EPFL), Kubilay Atasu (IBM Research - Z+APw-rich)

 A Roadmap for the GraphBLAS C+-+- API
 Benjamin A. Brock (UC Berkeley), Ayd+ATE-n Bulu+AOc- (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), Jos+AOk- E. Moreira (IBM)
Considerations for a Distributed GraphBLAS API
 Benjamin A. Brock (UC Berkeley), Ayd+ATE-n Bulu+AOc- (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), Jos+AOk- 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


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+IBk-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:

+ICI- 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+ADs-
+ICI- Discuss the problem domains and problems addressable with graph methods, machine learning methods, or both+ADs-
+ICI- Discuss programming models and associated frameworks such as Pregel, Galois, Boost, GraphBLAS, GraphChi, etc., for building large multi-attributed graphs+ADs-
+ICI- Discuss how frameworks for building graph algorithms interact with those for building machine learning algorithms+ADs-
+ICI- Discuss hardware platforms specialized for addressing large, dynamic, multi-attributed graphs and associated machine learning+ADs-

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.


General co-Chairs:

   Scott McMillan (CMU SEI), smcmillan+AEA-sei.cmu.edu
   Manoj Kumar (IBM), manoj1+AEA-us.ibm.com

Program Chairs:

   Danai Koutra (University of Michigan, Ann Arbor), dkoutra+AEA-umich.edu
   Mahantesh Halappanavar (PNNL), hala+AEA-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 Bulu+AOc-, 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 G+APM-mez, 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)
   Ayd+ATE-n Bulu+AOc- (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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