CALL FOR PAPERS
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GrAPL 2026: Workshop on Graphs, Architectures, Programming, and Learning
https://hpc.pnl.gov/grapl/
June 4, 2026
Co-Located with IPDPS 2026
New Orleans, LA, USA
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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.
MPORTANT DATES
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Position or full paper submission: February 1, 2026 AoE
Notification: February 28, 2026
Camera-ready: March 6, 2026
Workshop: May 25, 2026
PAPER SUBMISSIONS
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Submission site:
https://ssl.linklings.net/conferences/ipdps/?page=Submit&id=GrAPLWorkshopFullSubmission&site=ipdps2026
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
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* General co-Chairs
Nesreen K. Ahmed (Outshift by CISCO), nesahmed@cisco.com
Manoj Kumar (IBM), manoj1@us.ibm.com
* Program co-Chairs
Kathrin Hanauer (University of Vienna), kathrin.hanauer@univie.ac.at
Marco Minutoli (AMD), marco.minutoli@amd.com
* GrAPL's Little Helpers
Tim Mattson (Intel)
Scott McMillan (CMU SEI)
Antonino Tumeo (PNNL)
* Technical Program Committee
Sameh Abdulah, KAUST, SA
Benjamin Brock, Intel, US
Umit V. Catalyurek, Georgia Institute of Technology and Amazon AWS, US
Fabio Checconi, Intel, US
S.M. Ferdous, Pacific Northwest National Laboratory, US
Md Taufique Hussain, Wake Forest University, US
Kamer Kaya, Sabancı University, TR
Jehandad Khan, AMD, US
Johannes Langguth, Simula, NO
Roger Pearce, Lawrence Livermore National Laboratory, Texas A&M University, US
Mihail Popov, French Institute for Research in Computer Science and Automation, FR
Bradley Rees, NVIDIA, US
Francesco Silvestri, University of Padova, IT
Bora Uçar, French National Center for Scientific Research, LIP, ENS de Lyon, FR
Albert-Jan Yzelman, Huawei, CH
Other Members TBD
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