Hongyuan Liu

• Ph.D. (2022) College of William & Mary (Computer Science)
• M.S. (2016) The University of Hong Kong (Computer Science)
• B.E. (2013) Shandong University (Computer Science and Technology)

• Research Computing Committee Member

• 2027 IEEE International Symposium on High-Performance Computer Architecture (HPCA) Program Committee Member
• 2026 IEEE International Symposium on Workload Characterization (IISWC) Artifact Evaluation Chair
• 2026 ACM/IEEE International Symposium on Computer Architecture (ISCA) Session Chair
• 2026 ACM/IEEE International Symposium on Computer Architecture (ISCA) Program Committee Member
• ACM Transactions on Architecture and Code Optimization (TACO) Journal Reviewer
• ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS) Journal Reviewer
• 2026 IEEE International Conference on Distributed Computing Systems (ICDCS) Program Committee Member
• 2026 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) Program Committee Member, Student Research Competition Track
• 2026 IEEE International Symposium on High-Performance Computer Architecture (HPCA) Program Committee Member
• 3rd Workshop on Computer Architecture Modeling and Simulation (CAMS) at MICRO 2025 Program Committee Member
• 2025 IEEE International Symposium on High-Performance Computer Architecture (HPCA) Session Chair

Conference Proceeding

1. Chen, Z.; Ge, T.; Eeckhout, L.; Liu, H.; Huang, J. (2026). cuPTW: Leveraging Idle Compute Units for Massively Parallel GPU Page Table Walks. Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS) (vol. 10, pp. 1-26). ACM.
   https://doi.org/10.1145/3805633.
2. Zeng, B.; Wu, Z.; Liu, H.; Chen, X. (2026). DSTree: Data-Driven Synchronous Traversals for Decision Forests on GPUs. Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 401-415). IEEE.
   https://doi.org/10.1109/IPDPS65963.2026.00043.
3. Luo, W.; Chen, Y.; Yu, X.; Wang, Q.; Fan, R.; Liu, H.; Chu, X. (2026). ROME: Maximizing GPU Efficiency for All-Pairs Shortest Path via Taming Fine-Grained Irregularities. Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP) (pp. 204-217). ACM.
   https://doi.org/10.1145/3774934.3786461.
4. Ge, T.; Chu, X.; Liu, H. (2025). Interleaved Bitstream Execution for Multi-Pattern Regex Matching on GPUs. Proceedings of the 58th IEEE/ACM International Symposium on Microarchitecture (MICRO) (pp. 385-400). IEEE/ACM.
   https://doi.org/10.1145/3725843.3756052.
5. Zhao, H.; Huang, J.; Chen, Z.; Zhu, K.; Chen, D.; Ji, Z.; Liu, H. (2025). VESTA: A Secure and Efficient FHE-based Three-Party Vectorized Evaluation System for Tree Aggregation Models. Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS) (1 ed., vol. 9, pp. 1-26). ACM.
   https://dl.acm.org/doi/10.1145/3711707.
6. Zhu, K.; Wu, Z.; Liu, H. (2024). Efficient Point Cloud Analytics on Edge Devices. Proceedings of the 30th International Conference on Parallel and Distributed Systems (ICPADS) (pp. 382-389). IEEE.
   https://doi.org/10.1109/ICPADS63350.2024.00057.
7. Ge, T.; Zhang, T.; Liu, H. (2024). ngAP: Non-blocking Large-scale Automata Processing on GPUs. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Volume 1 (pp. 268-285). ACM.
   https://dl.acm.org/doi/10.1145/3617232.3624848.
8. Liu, H.; Pai, S.; Jog, A. (2023). Asynchronous Automata Processing on GPUs. Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS) (1 ed., vol. 7, pp. 1-27). ACM.
   https://dl.acm.org/doi/10.1145/3579453.
9. Liu, H.; Nicolae, B.; Di, S.; Cappello, F.; Jog, A. (2021). Accelerating DNN Architecture Search at Scale Using Selective Weight Transfer. Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER) (pp. 82-93). IEEE.
   https://doi.org/10.1109/Cluster48925.2021.00051.
10. Liu, H.; Pai, S.; Jog, A. (2020). Why GPUs are Slow at Executing NFAs and How to Make them Faster. Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (pp. 251-265). ACM.
    https://dl.acm.org/doi/10.1145/3373376.3378471.
11. Ibrahim, M. A.; Liu, H.; Kayiran, O.; Jog, A. (2019). Analyzing and Leveraging Remote-core Bandwidth for Enhanced Performance in GPUs. Proceedings of the 28th International Conference on Parallel Architectures and Compilation Techniques (PACT) (pp. 258-271). IEEE.
    https://doi.org/10.1109/PACT.2019.00028.
12. Liu, H.; Ibrahim, M. A.; Kayiran, O.; Pai, S.; Jog, A. (2018). Architectural Support for Efficient Large-Scale Automata Processing. Proceedings of the 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) (pp. 908-920). IEEE.
    https://dl.acm.org/doi/10.1109/MICRO.2018.00078.
13. Liu, H.; Lam, K.; Lin, H.; Wang, C.; Ma, J. (2016). Lightweight Dependency Checking for Parallelizing Loops with Non-Deterministic Dependency on GPU. Proceedings of the IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS) (pp. 884-893). IEEE.
    https://doi.org/10.1109/icpads.2016.0119.

Journal Article

1. Ge, T.; Zhang, T.; Liu, H. (2026). Towards Scalable and Non-blocking Automata Processing on GPUs with ngAP. ACM Transactions on Computer Systems (TOCS) (3 ed., vol. 44, pp. 1-33). ACM.
   https://doi.org/10.1145/3748646.
2. Wu, Z.; Ge, T.; Li, J.; Chen, X.; Liu, H. (2025). Advancing Matrix Operations for High-Performance and Memory-Efficient Automata Processing on GPUs. ACM Transactions on Architecture and Code Optimization (TACO) (4 ed., vol. 22, pp. 1-26). ACM.
   https://doi.org/10.1145/3774656.
3. Wu, Z.; Zhao, H.; Liu, H.; Wen, W.; Li, J. (2025). gHyPart: GPU-friendly End-to-End Hypergraph Partitioner. ACM Transactions on Architecture and Code Optimization (TACO) (1 ed., vol. 22, pp. 1-25). ACM.
   https://dl.acm.org/doi/10.1145/3711925.
4. Lin, H.; Wang, C.; Liu, H. (2018). On-GPU Thread-Data Remapping for Branch Divergence Reduction. ACM Transactions on Architecture and Code Optimization (TACO) (3 ed., vol. 15, pp. 1-24). ACM.
   https://dl.acm.org/doi/10.1145/3242089.