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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NEW: Position or full paper submission: February 8, 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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* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
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