[CFP] GrAPL 2026: Workshop on Graphs: Architectures, Programming, and Learning - co-located with IPDPS 2026

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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), [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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