[IPDPS] GrAPL 2024 Call for Papers

[Manoj Kumar]

GrAPL 2024: Workshop on Graphs, Architectures, Programming, and Learning
May 27, 2024
Co-Located with IPDPS 2024
San Francisco, California, US
Call for Papers
Data analytics is one of the fastest growing segments of computer science. Many real-world analytic workloads combine 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;
• Investigate novel solutions for accelerating graph learning-based methods using methodologies such as graph neural networks and knowledge graphs;
• Discuss graph programming models and associated frameworks such as GraphBLAS, Galois, Pregel, the Boost Graph Library, GraphChi, etc., for building large multi-attributed graphs;
• Discuss how frameworks for building graph algorithms interact with those for building machine learning algorithms;
• Discuss the convergence of graph analytics, frameworks, and graph databases;
• Discuss hardware platforms specialized for addressing large, dynamic, multi-attributed graphs and associated machine learning;
• Discuss the problem domains and applications of graph methods, machine learning methods, or both.

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 2, 2024 AoE
Notification: February 22, 2024
Camera-ready: February 29, 2024
Workshop: May 27, 2024
Submissions
Submission site: https://ssl.linklings.net/conferences/ipdps/?page=Submit&id=GrAPLWorkshopFullSubmission&site=ipdps2024

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.
Manuscript Templates for Conference Proceedings<http://www.ieee.org/conferences_events/conferences/publishing/templates.html>
Manuscript templates providing a consistent format for composing and formatting conference papers.
www.ieee.org

Organization

General co-Chairs
Nesreen K. Ahmed (Intel), nesreen.k.ahmed at intel.com
Manoj Kumar (IBM), manoj1 at us.ibm.com
 jannesar at iastate.edu
Program co-Chairs
Giulia Guidi (Cornell University), gg434 at cornell.edu
Ali Jannesari (Iowa State University),

GrAPL's Little Helpers
Tim Mattson (Retired)
Scott McMillan (CMU SEI)
Antonino Tumeo (PNNL)

Steering Committee
Nesreen K. Ahmed (Intel)
David A. Bader (New Jersey Institute of Technology)
Aydın Buluç (LBNL)
John Feo (PNNL)
John Gilbert (UC Santa Barbara)
Mahantesh Halappanavar (PNNL)
Tim Mattson (Intel)
Scott McMillan (CMU SEI)
Ananth Kalyanaraman (Washington State University)
Jeremy Kepner (MIT Lincoln Laboratory)
Danai Koutra (University of Michigan)
Manoj Kumar (IBM)
Antonino Tumeo (PNNL)

Technical Program Committee
Ariful Azad, Indiana University, US
Aydin Buluç, Lawrence Berkeley National Laboratory, US
Fabio Checconi, Intel, US
Aditya Devarakonda, Wake Forest University, US
Raqib Islam, UNC Charlotte, US
Kamesh Madduri, Pennsylvania State University, US
Arya Mazaheri, TU Darmstadt, Germany
José Moreira, IBM TJ Watson, US
Cindy Phillips, Sandia National Laboratories, US
Mihail Popov, Inria, FR
Yukinori Sato, Toyohashi University of Technology, JP
Catherine Schuman, University of Tennessee, US
Oguz Selvitopi, Lawrence Berkeley National Laboratory, US
Francesco Silvestri, University of Padua, IT
Flavio Vella, University of Trento, IT
Ana Lucia Verbanescu, University of Twente, NL
Albert-Jan Yzelman, Huawei, CH
Helen Xu, Lawrence Berkeley National Laboratory, US
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