I am currently a computer scientist at the Computational Science Initiative of Brookhaven National Laboratory. My research centers on computer architecture, parallel programming model, modeling and simulation, and machine learning, with a particular emphasis on high-performance computing. My CV can be found here.
Before joining BNL, I worked at the Department of Computer Science of Rutgers University as a Postdoc with Professor Eddy Z. Zhang to carry out GPGPU research between Sept. 2014 and Aug. 2016. I obtained my PhD degree in computer architecture from the Microprocessor Research and Development Center, Peking University in 2014.
Lingda Li, Thomas Flynn, Adolfy Hoisie. Learning Generalizable Program and Architecture Representations for Performance Modeling. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Atlanta, USA, 2024. (slides, video, code)
Tao Zhang, Lingda Li, Vanessa López-Marrero, Meifeng Lin, Yangang Liu, Fan Yang, Kwangmin Yu, Mohammad Atif. Emulator of PR-DNS: Accelerating Dynamical Fields with Neural Operators in Particle-Resolved Direct Numerical Simulation. Journal of Advances in Modeling Earth Systems (JAMES), 2024. (Editor’s Highlight)
Santosh Pandey, Lingda Li, Thomas Flynn, Adolfy Hoisie, Hang Liu. Scalable Deep Learning-Based Microarchitecture Simulation on GPUs. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Dallas, USA, 2022.
Lingda Li, Santosh Pandey, Thomas Flynn, Hang Liu, Noel Wheeler, Adolfy Hoisie. SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning. Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS/PERFORMANCE), Mumbai, India, 2022. (slides, video, code)
Heng Zhang, Lingda Li, Hang Liu, Donglin Zhuang, Rui Liu, Chengying Huan, Shuang Song, Dingwen Tao, Yongchao Liu, Charles He, Yanjun Wu, Shuaiwen Leon Song. Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator System. Proceedings of the 36th ACM International Conference on Supercomputing (ICS), 2022.
Santosh Pandey, Zhibin Wang, Sheng Zhong, Chen Tian, Bolong Zheng, Xiaoye Li, Lingda Li, Adolfy Hoisie, Caiwen Ding, Dong Li, Hang Liu. TRUST: Triangle Counting Reloaded on GPUs. IEEE Transactions on Parallel and Distributed Systems, 32(11):2646–2660, 2021.
Santosh Pandey, Lingda Li, Adolfy Hoisie, Hang Liu. C-SAW: A Framework for Graph Sampling and Random Walk on GPUs. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Atlanta, USA, Nov. 15-20, 2020.
Lingda Li, Barbara Chapman. Compiler Assisted Hybrid Implicit and Explicit GPU Memory Management under Unified Address Space. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Denver, USA, Nov. 17-22, 2019. (slides)
Lingda Li, Hal Finkel, Martin Kong, Barbara Chapman. Manage OpenMP GPU Data Environment under Unified Address Space. Proceedings of the 14th International Workshop on OpenMP (IWOMP), Barcelona, Spain, Sept. 27-28, 2018. (slides)
Alok Mishra, Lingda Li, Martin Kong, Hal Finkel, Barbara Chapman. Benchmarking and Evaluating Unified Memory for OpenMP GPU Offloading. Proceedings of the Fourth Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC), Denver, USA, Nov. 12-17, 2017. (slides)
Ari B. Hayes, Lingda Li, Mohammad Hedayati, Jiahuan He, Eddy Z. Zhang, Kai Shen. GPU Taint Tracking. Proceedings of the 2017 USENIX Annual Technical Conference (ATC), Santa Clara, USA, Jul. 12-14, 2017. (slides)
Lingda Li, Robel Geda, Ari B. Hayes, Yanhao Chen, Pranav Chaudhari, Eddy Z. Zhang, Mario Szegedy. A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing. Proceedings of the ACM SIGMETRICS Conference (SIGMETRICS '17) and POMACS, Urbana-Champaign, USA, Jun. 5-9, 2017. (slides)
Lingda Li, Ari B. Hayes, Shuaiwen L. Song, and Eddy Z. Zhang. Tag-Split Cache for Efficient GPGPU Cache Utilization. Proceedings of the 30th ACM International Conference on Supercomputing (ICS), Istanbul, Turkey, Jun. 1-3, 2016. (slides, code)
Ari B. Hayes, Lingda Li, Daniel Chavarria, Shuaiwen Leon Song, and Eddy Z. Zhang. ORION: A Framework for GPU Occupancy Tuning. Proceedings of the 17th Annual Middleware Conference, Trento, Italy, Dec. 12-16, 2016.
Lingda Li, Junlin Lu, and Xu Cheng. Block value based insertion policy for high performance last-level caches. Proceedings by the 28th ACM International Conference on Supercomputing (ICS), Munich, Germany, Jun. 10-13, 2014. (slides)
Lingda Li, Junlin Lu, and Xu Cheng. Retention benefit based intelligent cache replacement. Journal of Computer Science and Technology (JCST), 29(6):947-961, 2014.
Lingda Li, Dong Tong, Zichao Xie, Junlin Lu, and Xu Cheng. Improving inclusive cache performance with two-level eviction priority. Proceedings of the 30th IEEE International Conference on Computer Design (ICCD), Montreal, Canada, Sept. 30-Oct. 3, 2012. (slides)
Lingda Li, Dong Tong, Zichao Xie, Junlin Lu, and Xu Cheng. Optimal bypass monitor for high performance last-level caches. Proceedings of the 21th ACM/IEEE International Conference on Parallel Architectures and Compilation Techniques (PACT), Minneapolis, USA, Sept. 19-23, 2012. (slides)
Program committee member of SC 2023 & 2024, IPDPS 2024 & 2025, SIGMETRICS 2023 & 2025, ICS 2025, ICPP 2021 & 2023, ICPADS 2016 & 2017, AE CGO-PPoPP 2015 & 2016
Reviewer of TC, TPDS, TKDE, TSUSC, TOPC, TACO, IJHPCA, CGO 2015, PLDI 2015, CCGrid 2015, HPDC 2016, ICAC 2016, ICS 2016, ICPP 2016, SC 2016, NPC 2017, SC 2018, EuroPar 2019, IWOPH 2019
Mentor of BNL Hackathon 2017, 2018, 2019