Extended Deadline - GrAPL 2026: Workshop on Graphs: Architectures, Programming, and Learning - co-located with IPDPS 2026

[Please accept our apologies for multiple postings.]

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), [email protected]
Manoj Kumar (IBM), [email protected]

* Program co-Chairs

Kathrin Hanauer (University of Vienna), [email protected]
Marco Minutoli (AMD), [email protected]

* 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
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*   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
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