GraphSys '26 also features three high-impact challenges that steer research toward causal and temporal reasoning in parallel and distributed systems. 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.

Causal discovery in HPC and datacenter management
GraphSys Workshop / EuroPar'26

Challenge 1: Causal discovery in HPC and datacenter management

We focus on how causal relationships can be discovered, inferred, and leveraged for HPC and datacenter management. We welcome research on:

  • Causal modeling and reasoning
  • Causal inference
  • Causal explainable artificial intelligence

Understanding not only what correlates with performance or failures but why it does is essential for robust resource management, scheduling, and root-cause analysis at scale. Causal discovery in this setting can inform capacity planning, fault tolerance, and energy optimization.

Papers of interest

  • Gonzalez, D. Z., Meyer, M., & Muller, O. (2025). Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study. ACM SIGENERGY Energy Informatics Review, 5(2), 4-10.
  • Bi, T., Yang, Z., Pan, Y., Zhang, Y., Ma, M., Jiang, X., ... & Wang, P. (2024, August). Faultinsight: Interpreting hyperscale data center host faults. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 141-152).
  • Prakash, P., Hong Enriquez, R. P., Serebryakov, S., Grant, D., Brewer, W., & Milojicic, D. (2025, November). From Exploration to Explanation: ML-Driven Causal Discovery for Datacenter Reliability at Scale. In Proceedings of the SC'25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 997-1002).

Datasets of interest

  • M100 ExaData (CINECA Marconi100) – holistic monitoring data (management, workload, facility, and infrastructure) from the Marconi100 Tier-0 supercomputer, collected via the ExaMon framework.
  • Google cluster data – traces of jobs, machine states, and resource usage from production Google compute clusters, widely used for systems and scheduling research.
Causal understanding of quantum uncertainty
GraphSys Workshop / EuroPar'26

Challenge 2: Causal understanding of quantum uncertainty

We focus on how processing information from quantum computations could be leveraged to discover causal relationships between qubit failures and the events that lead to them. Modeling these as causal structures rather than purely statistical phenomena can guide better noise/error mitigation, calibration, and system design.

Papers of interest

  • Shaib, A., Naim, M. H., Fouda, M. E., Kanj, R., & Kurdahi, F. (2023). Efficient noise mitigation technique for quantum computing. Scientific Reports, 13(1), 3912.
  • Nguyen, H. Q., Nguyen, X. B., Chen, S. Y. C., Churchill, H., Borys, N., Khan, S. U., & Luu, K. (2025). Diffusion-inspired quantum noise mitigation in parameterized quantum circuits. Quantum Machine Intelligence, 7(1), 55.
  • Wang, H., Gu, J., Ding, Y., Li, Z., Chong, F. T., Pan, D. Z., & Han, S. (2022, July). Quantumnat: quantum noise-aware training with noise injection, quantization and normalization. In Proceedings of the 59th ACM/IEEE design automation conference (pp. 1-6).
  • Placidi, L., Williams, I., Rinaldi, E., Mills, D., Cirstoiu, C., Eccles, V., & Duncan, R. (2026). Deep Learning Approaches to Quantum Error Mitigation. arXiv preprint arXiv:2601.14226.

Datasets of interest

  • QuaN - a collection of specially designed datasets for exploring the impact of noise in quantum machine learning and other applications. The work focuses on transforming clean datasets into noisy counterparts across diverse domains, including MNIST handwritten digits datasets, Medical MNIST, IRIS datasets, and Mobile Health datasets. The dataset is created using noise from classical and quantum domains. Classical noise includes Gaussian distribution, salt and pepper method, random perturbation, class imbalance, and missing values, while quantum noise includes bitflip, phase flip, and amplitude damping, among others.
  • QDataSet - a quantum dataset designed to facilitate the training and development of QML algorithms. QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise.
Causal hardware–software co-design
GraphSys Workshop / EuroPar'26

Challenge 3: Causal hardware–software co-design

We focus on understanding causal relationships between hardware design (components and layout) and how these affect processing capabilities at the software level. Rather than treating hardware and software in isolation, we aim to foster work that explicitly models and reasons about cause-effect links: for example, how layout, memory hierarchy, or interconnect design causally influence throughput, latency, or energy. We also welcome AI4EDA research, focused on a particular stage of this domain (not only co-design), that includes causal components to achieve its goals. This perspective is still underexplored and can be highly innovative, bringing together architecture, systems, and causal reasoning communities.

Papers of interest

  • El Sayed, Z., Wang, Z., Selmani, H., Knechtel, J., Sinanoglu, O., & Alrahis, L. (2025). Graph neural networks for integrated circuit design, reliability, and security: Survey and tool. ACM Computing Surveys, 58(4), 1-44.
  • Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., ... & Dean, J. (2021). A graph placement methodology for fast chip design. Nature, 594(7862), 207-212.
  • Cheng, C. K., Kahng, A. B., Kang, I., & Wang, L. (2018). Replace: Advancing solution quality and routability validation in global placement. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(9), 1717-1730.
  • Chen, H., Qi, Z., He, Y., Xu, Z., Qin, J., & Zhao, T. (2025, August). GranuTopoFormer: Multi-Granular Embedding and Topology-Aware Transformer for Analog Circuit Parameter Prediction. In 2025 IEEE 17th International Conference on Electronic Measurement & Instruments (ICEMI) (pp. 384-389). IEEE.
  • Xu, K., Schwachhofer, D., Blocklove, J., Polian, I., Domanski, P., Pfluger, D., ... & Li, B. (2025, September). Large Language Models (LLMs) for Electronic Design Automation (EDA): Special Session Paper. In 2025 IEEE 38th International System-on-Chip Conference (SOCC) (pp. 1-6). IEEE.
  • Zadeh, D. N., & Elamien, M. B. (2025). Generative AI for analog integrated circuit design: Methodologies and applications. IEEE Access.

Projects of interest

  • Google AlphaChip - an open-source framework for generating chip floorplans with distributed deep reinforcement learning.
  • OpenROAD - the leading open-source, foundational application for semiconductor digital design. The OpenROAD flow delivers an Autonomous, No-Human-In-Loop (NHIL) flow, 24 hour turnaround from RTL-GDSII for rapid design exploration and physical design implementation.

Submission types for the challenges

  • Extended abstracts (2 pages): directly addressing one or more of the challenges; ideal for vision papers or early-stage ideas.
  • Full papers (10–12 pages) and short papers (6 pages): we welcome both methodological and applied contributions that tackle these challenges with concrete models, algorithms, or empirical results.
  • Datasets: we encourage the submission or dissemination of FAIR datasets that support causal and temporal analysis related to the abovementioned challenges.

For full submission details and important dates, see the Call for Papers.