[hpc-announce] [CFP] GrAPL 2021 - co-located with IPDPS 2021

Tumeo, Antonino Antonino.Tumeo at pnnl.gov
Wed Jan 13 10:31:51 CST 2021

[Please accept our apologies for multiple postings.]



GrAPL 2021: Workshop on Graphs, Architectures, Programming, and Learning

May 17, 2021
Co-Located with IPDPS 2021
Oregon, 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


Call for Papers
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 1, 2021 
Notification: February 28, 2021
Camera-ready: March 15, 2021
Workshop: May 17, 2021


Submission site: https://ssl.linklings.net/conferences/ipdps/?page=Submit&id=GrAPLWorkshopFullSubmission&site=ipdps2021

Please visit the GrAPL'21 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 Chair

Nesreen Ahmed  (Intel), nesreen.k.ahmed at intel.com

GrAPL's Little Helpers

Tim Mattson (Intel)
Antonino Tumeo (PNNL)
Steering Committe
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)
Ananth Kalyanaraman (Washington State University)
Jeremy Kepner (MIT Lincoln Laboratory)
Danai Koutra (University of Michigan)
Antonino Tumeo (PNNL)

Technical Program Committee

Paul Bogdan, University of Southern California , US
Anu Bourgeois, Georgia State University , US
Aydin Buluç, Lawrence Berkeley National Laboratory; University of California, Berkeley, US
Sergio Gomez, Universitat Rovira i Virgili , ES
Stratis Ioannidis, Northeastern University, US
Kamesh Madduri, Pennsylvania State University , US
Hesham Mostafa, Intel Labs, US
Robert Rallo, Pacific Northwest National Laboratory, US
Indranil Roy, Natural Intelligence Systems, Inc. , US
Ponnuswamy Sadayappan, University of Utah; Pacific Northwest National Laboratory, US
Shaden Smith, Microsoft Corporation, US
Yizhou Sun, University of California, Los Angeles, US
Ramachandran Vaidyanathan, Louisiana State University , US
Alexander van der Grinten, Humboldt-University of Berlin , DE
Flavio Vella, Free University of Bozen, IT

Other members TBD

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