PNNL

Luanzheng (Lenny) Guo is a computer scientist in Pacific Northwest National Laboratory’s High-Performance Computing (HPC) group, working within the research area between scientific computing, data management, large-scale systems (e.g., HPC, CPS, Cloud, Edge, etc.), and machine learning (ML). 

Guo is serving as the principle investigator for the Generative AI Laboratory Directed Research and Development (LDRD) High-performance Generative AI-based Microscope Data Compression project. He is also the co-principle investigator for the Generative AI LDRD Generative AI for Building High-value and Informative Machine Operations Datasets project and the Resilience Through Data-Driven, Intelligently Designed Control LDRD Adaptive Learning-Enabled Resilient Tuning 2.0: Predictive Risk Informed Data-driven Resilient Controls project.

Guo obtained his PhD in electrical engineering and computer science from the University of California, Merced. His PhD research focused on system resilience and reliability in HPC systems. 

Guo received two outstanding performance awards by Pacific Northwest National Laboratory in 2024 in recognition of both his contributions to scholarship, proposals, and collaborations activities and his mentoring and developing of emerging talent. His paper was nominated for best paper at the IEEE International Conference on Cluster Computing 2024. His poster was nominated for best poster at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) 2016, the most prestigious conference in HPC. He won outstanding lightning talk in SC 2018, where he was also recognized as an outstanding student volunteer, and was a lead student volunteer at SC 2019. His research was featured by HPCwire in its "What's New in HPC Research" feature in 2018 and 2020. He was a National Science Foundation Trusted Cyberinfrastructure Fellow of 2020.

• Scientific workflow management
• Data management
• Data compression
• Database systems
• AI 4 Science
• Automated experiments
• Carbon neutralization/reduction
• Complex systems including HPC, CPS, Cloud, Edge, etc.
• Cybersecurity
• Generative AI
• Graph analytics
• Performance optimization
• Physics-informed neural networks/ML
• Science automation
• System resilience
• System software

• C/C++ STL
• CMake
• Compiler optimization
• Digital image processing
• Fault tolerant systems
• Graph analytics
• HPC
• Hypergraph theory
• ML algorithms
• Message passing interface
• OpenMP
• Python
• Resilience systems
• Workflow management systems

• PhD in computer science and engineering, University of California, Merced
• MS in computer technology, Nanchang HangKong University
• BS in network engineering, Nanchang HangKong University

• Association for Computing Machinery (ACM)
• Institute of Electrical and Electronics Engineers
• ACM Special Interest Group on High Performance Computing

• Outstanding Performance Award, in recognition of contributions to scholarship, proposals, and collaborations activities, Pacific Northwest National Laboratory (2024)
• Outstanding Performance Award, in recognition of mentoring and developing emerging talent,  Pacific Northwest National Laboratory (2024)
• Best Paper finalist, Cluster’24 (2024)
• Trusted Cyberinfrastructure Fellow, National Science Foundation (2020)
• Outstanding Lightning Talk award, Outstanding Student Volunteer SC’18 (2018)
• Best Poster finalist, SC’16 (2016)

2025

• Abebe W.M., J.F. Strube, L. Guo, N.R. Tallent, O. Bel, S.R. Spurgeon, and C.M. Doty, et al. 2025. "SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation." Computational Materials Science 246, Art. No. 113400. PNNL-SA-194572. doi:10.1016/j.commatsci.2024.113400

2024

• Ashraf R.A., L. Guo, H. Lee, and N.R. Tallent. 2024. "Identifying Outliers in AI-based Image Compression." In IEEE International Conference on Big Data (BigData 2024), December 15-18, 2024, Washington, D.C., 8611-8613. Los Alamitos, California: IEEE Computer Society. PNNL-SA-204518.  doi:10.1109/BigData62323.2024.10825013
• Fu, X. L. Guo, et al. 2024 “Distributed Order Recording Techniques for Efficient Record-and-Replay of Multi-threaded Programs,” 2024 IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan. (Best paper finalist)
• Fu, X., W. Zhang, X. Huang, W. Xu, S. Meng, L. Guo, and K. Sato. 2024. “AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis,” SC ’24: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA
• Fu, X., X. Huang, W. Xu, S. Meng, W. Zhang, L. Guo, and K. Sato. 2024. “Benchmarking variables for checkpointing in HPC Applications,” 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), San Francisco, CA, USA
• Guo L., M. Tang, H. Lee, J.S. Firoz, and N.R. Tallent. 2024. "Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis." In IEEE International Conference on Big Data workshop BPOD 2024. PNNL-SA-205879.
• Huang X., W. Xu, S. Meng, W. Zhang, X. Fu, L. Guo, and K. Sato. 2024. "Scrutinizing Variables for Checkpoint Using Automatic Differentiation." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. PNNL-SA-204754.
• Lee H., J.S. Firoz, N.R. Tallent, L. Guo, and M. Halappanavar. 2024. "FlowForecaster: Automatically Inferring Detailed & Interpretable Workflow Scaling Models for Better Scheduling." In Proceeding of the 39th IEEE International Parallel and Distributed Processing Symposium. PNNL-SA-207132.
• Mamud M., M.K. Mudunuru, K.B. Nakshatrala, B. Ghanbarian, T. Varga, L. Guo, and S. Karra. 12/11/2024. "Unlocking Porous Secrets: Harnessing Physics-Informed Machine Learning for Fluid Flow Modeling in Soil Cores." Abstract submitted to 2024 AGU Annual Meeting, Washinton, Dc, District Of Columbia. PNNL-SA-201465.
• Naraparaju R., T. Zhao, Y. Hu, D. Zhao, L. Guo, and N.R. Tallent. 2024. "Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. PNNL-SA-203969.
• Tang, M., J. Cernuda, J. Ye, L. Guo, N. R. Tallent, A. Kougkas, and X.-H. Sun. 2024. “DaYu: Optimizing Distributed Scientific Workflows by Decoding Dataflow Semantics and Dynamics,” 2024 IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan.
• Xu W., X. Huang, S. Meng, W. Zhang, L. Guo, and K. Sato. 2024. "An Efficient Checkpointing System for Large Machine Learning Model Training." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, November 17-22, 2024, Atlanta, GA, 896-900. Piscataway, New Jersey: IEEE. PNNL-SA-204755.
• Zhang B., L. Guo, J. Tian, J. Liu, D. Wang, F. Ye, and C. Zhang, et al. 2024. "ViSemZ: High-performance Visual Semantics Compression for AI-Driven Science." In ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming. PNNL-SA-202887.

2023

• Fu, X., et al. 2023. "A High-dimensional Algorithm-Based Fault Tolerance Scheme," 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), St. Petersburg, FL, USA, pp. 326-330, doi: 10.1109/IPDPSW59300.2023.00061
• Gioiosa, R., E. Apra, A. Marquez, A. R. Panyala, R. A. Ashraf, and L. Guo. 2023. "Navier: Dataflow Architecture for Computation Chemistry.” doi:10.2172/2203460
• Guo, L., et al. 2023. “Understanding System Resilience for Converged Computing of Cloud, Edge, and HPC,” ISC High Performance 2023, vol 13999, doi: 10.1007/978-3-031-40843-4_17
• Guo, L., R. A. Ashraf, R. D. Friese, and G. Kestor. 2023. “Towards Supporting Semiring in MLIR-Based COMET Compiler,” In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT '22). Association for Computing Machinery, New York, NY, USA, 542–543, doi:10.1145/3559009.3569683
• Guo, L. and G. Kestor. 2023. "On Higher-performance Sparse Tensor Transposition," 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), St. Petersburg, FL, USA, pp. 697-701, doi: 10.1109/IPDPSW59300.2023.00118
• Guo, L., J. Firoz, and G. Kestor. 2023. “LAHypergraph: Parallel Hypergraph Analytics in the Language of Linear Algebra,” SIAM Conference on Applied and Computational Discrete Algorithms (ACDA23), pp. 147-158, doi: 10.1137/1.9781611977714.13
• Lee, H., L. Guo, M. Tang, J. S. Firoz, N. R. Tallent, A. Kougkas, and X. Sun. 2023. "Data Lifecycles: Optimizing Workflow Task & Data Coordination," In SC '23: The International Conference for High Performance Computing, Networking, Storage, and Analysis. New York, NY, United States, New York: Association for Computing Machinery. PNNL-SA-185677. doi:10.1145/3581784.3607104
• Lu, S., J. Chu, L. Guo, and X. T. Liu. 2023. “Im2win: An Efficient Convolution Paradigm on GPU,” Euro-Par 2023: Parallel Processing, vol 14100, doi:10.1007/978-3-031-39698-4_40
• Peng, Z., R. A. Ashraf, L. Guo, R. Tian, and G. Kestor. 2023. "Automatic Code Generation for High-Performance Graph Algorithms," 2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT), Vienna, Austria, pp. 14-26, doi: 10.1109/PACT58117.2023.00010

2022

• Li, Z. et al. 2022. "A Visual Comparison of Silent Error Propagation," in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2022.3230636

2021

• Guo, L., D. Li, and I. Laguna. 2021. “PARIS: Predicting application resilience using machine learning,” Journal of Parallel and Distributed Computing, Volume 152, pp. 111-124, doi:10.1016/j.jpdc.2021.02.015
• Tian, R., L. Guo, J. Li, B. Ren, and G. Kestor. 2021. "A High Performance Sparse Tensor Algebra Compiler in MLIR." In IEEE/ACM 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC 2021), November 14, 2021, St. Louis, MO, 27-38. Piscataway, NJ: IEEE. PNNL-SA-168094. doi:10.1109/LLVMHPC54804.2021.00009

2020

• Georgakoudis, G., L. Guo, and I. Laguna. 2020. “Reinit ⁺⁺ Evaluating the Performance of Global-Restart Recovery Methods for MPI Fault Tolerance,” ISC High Performance 2020, vol 12151, doi:10.1007/978-3-030-50743-5_27
• Guo, L., G. Georgakoudis, K. Parasyris, I. Laguna, and D. Li. 2020. "MATCH: An MPI Fault Tolerance Benchmark Suite," 2020 IEEE International Symposium on Workload Characterization (IISWC), Beijing, China, pp. 60-71, doi: 10.1109/IISWC50251.2020.00015

2019

• Guo, L. and D. Li. 2019. "MOARD: Modeling Application Resilience to Transient Faults on Data Objects," 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, pp. 878-889, doi: 10.1109/IPDPS.2019.00096

2018

• Guo, L., D. Li, I. Laguna, and M. Schulz. 2018. "FlipTracker: Understanding Natural Error Resilience in HPC Applications," SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, Dallas, TX, USA, pp. 94-107, doi: 10.1109/SC.2018.00011

2013

• Chu, J., A. GuoLu, and L. Wang. 2013. “Chessboard corner detection under image physical coordinate,” Optics & Laser Technology, Volume 48, Pages 599-605, doi:10.1016/j.optlastec.2012.11.010
• Chu, J., A. GuoLu, L. Wang, C. Pan, and S. Xiang. 2013. "Indoor frame recovering via line segments refinement and voting," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, pp. 1996-2000, doi: 10.1109/ICASSP.2013.6638003