-------- Forwarded Message -------- Subject: [computational.science] [CFP] GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning (co-located with IPDPS 2020) Date: Fri, 10 Jan 2020 17:49:26 +0000 From: Tumeo, Antonino via computational.science computational.science@lists.iccsa.org Reply-To: Tumeo, Antonino Antonino.Tumeo@pnnl.gov To: ascgroup@gmail.com ascgroup@gmail.com

[Please accept our apologies for multiple postings.] CALL FOR PAPERS ******************************************************************************* 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 ****************************************************************************** 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. IMPORTANT DATES --------------- Position or full paper submission: February 3, 2020 Author Notification: February 29, 2020 Camera-ready: March 15, 2020 Workshop: May 18, 20120 PAPER SUBMISSIONS ----------------- 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 ------------ 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)