Aligned with Euro-Par 2026, the premier European conference addressing all facets of parallel and distributed
processing, GraphSys ‘26 is the Fourth Workshop on Serverless, Extreme-Scale, and Sustainable Graph
Processing Systems. GraphSys is a cross-disciplinary meeting venue focusing on state-of-the-art and emerging
(future) graph processing systems, with an emphasis on causality and temporal graphs.
The workshop is designed for researchers, practitioners, and students interested in sustainable graph processing.
Participants from academia, industry, government research labs, and nonprofit organizations are encouraged to
attend and contribute to the GraphSys discussions and collaborations.
===Topics of interest for GraphSys’26===
• System architectures and system designs for graph creation, enrichment, graph query, and graph
analytics.
• Programming and data-level interfaces, APIs, and models for graph processing.
• Benchmarks, performance monitoring, measurement techniques, methods, metrics, tools, and
instruments for graph processing.
• Sustainability metrics, tools, and instruments for real-world, analytical, and simulation-based approaches
to performance analysis for graph processing systems.
• Parallel and distributed (including heterogeneous) algorithms and methods for graph processing.
• Serverless techniques, addressing graph processing challenges of full-automation software and data
services with fine granularity and utilization-based billing.
• Empirical case studies of graph processing environments, applications, and systems, including
comparative performance studies and benchmarking of real-world and production graph-processing
systems.
• Methodological aspects of software engineering, performance engineering, and computer systems related
to graph processing.
• Development and dissemination of FAIR datasets that provide the rationale for system phenomena,
including intervention-based logs for root-cause analysis in HPC and quantum hardware.
===Challenges===
GraphSys ’26 also features three high-impact challenges that steer research toward causal and temporal reasoning
in parallel and distributed systems.
• Challenge 1: Causal discovery in HPC and datacenter management
• Challenge 2: Causal understanding of quantum uncertainty
• Challenge 3: Causal hardware–software co-design
We invite submissions that push the boundaries of the field – whether through foundational vision papers, the
release of novel datasets, benchmarks, or rigorous academic and industrial advancements addressing our three
core challenges. Extended abstracts (2 pages) addressing one or more of the challenges are welcome in addition
to full and short papers.
All submissions are required in PDF format and must be sent via EasyChair.
We look forward to receiving your contributions!