Jie Li

Research Assistant, Department of Computer Science, Texas Tech University

Email: jie[dot]li[at]ttu[dot]edu

Homepage: https://lijie.me


Doctor of Philosophy, Computer Science, Texas Tech University, Lubbock, Texas

Master of Science, Computer Science, Texas Tech University, Lubbock, Texas


My research interests lie in the field of High-Performance Computing (HPC), encompassing HPC systems monitoring, automation, and management, operational data analytics, job scheduling, and system architecture. I also have a keen interest in parallel and distributed computing and computer architecture.


Research Assistant

Data-Intensive Scalable Computing Laboratory (DISCL), TTU, Lubbock, Texas

Graduate Student Intern

Lawrence Berkeley National Laboratory (LBNL), Berkeley, California

Graduate Student Programmer

Teaching, Learning and Professional Development Center (TLPDC),TTU, Lubbock, Texas


Scheduling and Allocation of Disaggregated Memory Resources in HPC Systems

Monitoring Data Management and Query Performance Optimization

High-Performance Computing System Health Monitoring & Performance Data Collection


J. Li et al., “Scheduling and Allocation of Disaggregated Memory Resources in HPC Systems,” submitted to 38th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2024)
J. Li et al., “Towards Cycle-accurate Simulation of xBGAS,” submitted to 2024 International Conference on Computing, Networking and Communications (ICNC 2024)
J. Li, B. Cook, and Y. Chen, “ARcode: HPC application recognition through image-encoded monitoring data,” arXiv preprint arXiv:2301.08612, 2023, doi: https://doi.org/10.48550/arXiv.2301.08612.


X. Wang, A. Tumeo, J. D. Leidel, J. Li, and Y. Chen, “MAC: Memory access coalescer for 3D-stacked memory,” in Proceedings of the 48th international conference on parallel processing (ICPP’19), 2019, pp. 1–10. doi: https://doi.org/10.1145/3337821.3337867.
J. Li, X. Wang, A. Tumeo, B. Williams, J. D. Leidel, and Y. Chen, “PIMS: A lightweight processing-in-memory accelerator for stencil computations,” in Proceedings of the international symposium on memory systems (MemSys’19), 2019, pp. 41–52. doi: https://doi.org/10.1145/3357526.3357550.
V. Pham, N. Nguyen, J. Li, J. Hass, Y. Chen, and T. Dang, “Mtsad: Multivariate time series abnormality detection and visualization,” in 2019 IEEE international conference on big data (BigData’19), IEEE, 2019, pp. 3267–3276. doi: https://doi.org/10.1109/BigData47090.2019.9006559.
N. Nguyen, J. Hass, Y. Chen, J. Li, A. Sill, and T. Dang, “Radarviewer: Visualizing the dynamics of multivariate data,” in Practice and experience in advanced research computing (PEARC’20), 2020, pp. 555–556. doi: https://doi.org/10.1145/3311790.3404538.
J. Li et al., “Monster: An out-of-the-box monitoring tool for high performance computing systems,” in 2020 IEEE international conference on cluster computing (CLUSTER’20), IEEE, 2020, pp. 119–129. doi: https://doi.org/10.1109/CLUSTER49012.2020.00022.
X. Wang, A. Tumeo, J. D. Leidel, J. Li, and Y. Chen, “HAM: Hotspot-aware manager for improving communications with 3D-stacked memory,” IEEE Transactions on Computers (IEEE Trans Comput), vol. 70, no. 6, pp. 833–848, 2021, doi: https://doi.org/10.1109/TC.2021.3066982.
T. Dang, N. Nguyen, J. Hass, J. Li, Y. Chen, and A. Sill, “The gap between visualization research and visualization software in high-performance computing center,” The Gap between Visualization Research and Visualization Software (VisGap’21)), 2021, doi: https://doi.org/10.2312/visgap.20211089.
T. Dang, N. V. Nguyen, J. Li, A. Sill, J. Hass, and Y. Chen, “JobViewer: Graph-based visualization for monitoring high-performance computing system,” in 2022 IEEE/ACM international conference on big data computing, applications and technologies (BDCAT’22), IEEE, 2022, pp. 110–119. doi: https://doi.org/10.1109/BDCAT56447.2022.00021.
J. Li, G. Michelogiannakis, B. Cook, D. Cooray, and Y. Chen, “Analyzing resource utilization in an HPC system: A case study of NERSC’s perlmutter,” in International conference on high performance computing (ISC’23), Springer, 2023, pp. 297–316. doi: https://doi.org/10.1007/978-3-031-32041-5_16.
J. Li, R. Wang, G. Ali, T. Dang, A. Sill, and Y. Chen, “Workload failure prediction for data centers,” in 2023 IEEE 16th international conference on cloud computing (CLOUD’23), 2023, pp. 479–485. doi: https://doi.org/10.1109/CLOUD60044.2023.00064.
C. E. Caon, J. Li, and Y. Chen, “Effective management of time series data,” in 2023 IEEE 16th international conference on cloud computing (CLOUD’23), 2023, pp. 408–414. doi: https://doi.org/10.1109/CLOUD60044.2023.00055.


Conference Presentations

Research Seminar Talks


Undergraduate Students (including REU participants)

Graduate Students


Paper Reviewer


Last Update: November 4, 2023