PNNL

Bo Fang has worked as a research associate at Pacific Northwest National Laboratory (PNNL) since April 2020. While obtaining his PhD, his research focused on high-performance computing in general, with an emphasis on error-resilient systems. He currently works on projects include building efficient quantum computer simulation framework and large-scale resilient systems for emerging applications.

Fang’s research has been published in various conferences and journals. Recently, he received the Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Award, ranked as second in the computer science division. He is the recipient of the 2020 William C. Carter PhD Dissertation Award in Dependability and received an honorable mention for the 2020 Association for Computing Machinery’s (ACM’s) Special Interest Group on High-Performance Computing (SIGHPC) Doctoral Dissertation Award.

• Distributed systems
• High-performance computing (HPC)
• Dependable systems and fault tolerance techniques
• GPGPU computing
• Quantum computing

• C++
• Fault-tolerant systems
• HPC
• Python

• PhD in computer engineering, the University of British Columbia, Canada
• MAS in electrical and computer engineering, the University of British Columbia, Canada
• MS in software systems, the University of British Columbia, Canada
• BE in information security management, Wuhan University, China

• Honorable mention for the ACM SIGHPC Dissertation Award (2020)
• William C. Carter PhD Dissertation Award in Dependability (2020)
• NSERC Postdoctoral Fellowship Award (2020)

2022

• Baheri B., J. Tronge, B. Fang, A. Li, V. Chaudhary, and Q. Guan. 2022. "MARS: Malleable Actor-Critic Reinforcement Learning Scheduler." In Proceedings of the 41st International Performance Computing and Communications Conference (IPCCC 2022), November 11-13, 2022, Austin, TX, 217-226. Piscataway, New Jersey:IEEE. PNNL-SA-170367. doi:10.1109/IPCCC55026.2022.9894315
• Fang B., Y. Ozkaya, A. Li, U. Catalyurek, and S. Krishnamoorthy. 2022. "Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning." In IEEE International Conference on Cluster Computing (CLUSTER 2022), September 5-8, 2022, Heidelberg, Germany, 289-300. Piscataway, New Jersey:IEEE. PNNL-SA 170183. doi:10.1109/CLUSTER51413.2022.00041
• Narayanan N., Z. Chen, B. Fang, G. Li, K. Pattabiram, and N.A. Debardeleben. 2022. "Fault Injection for TensorFlow Applications." IEEE Transactions on Dependable and Secure Computing. PNNL-SA-161122. doi:10.1109/TDSC.2022.3175930
• Tan C., T. Tambe, J. Zhang, B. Fang, T. Geng, G. Wei, and D. Brooks, et al. 2022. "ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs." In Proceedings of the 36th ACM International Conference on Supercomputing (ICS 2022), June 28-30, 2022, Virtual, Online, Paper No. 4. New York, New York:Association for Computing Machinery. PNNL-SA-172791. doi:10.1145/3524059.3532359

2021

• Baheri B., D. Chen, B. Fang, S.A. Stein, V. Chaudhary, Y. Mao, and S. Xu, et al. 2021. "TQEA: Temporal Quantum Error Analysis." In 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S 2021), June 21-24, 2021, Taipei, Taiwan, 65-67. Piscataway, New Jersey:IEEE. PNNL-SA-159972. doi:10.1109/DSN-S52858.2021.00034
• Fang B., D. Wang, S. Jin, Q. Koziol, Z. Zhang, Q. Guan, and S. Byna, et al. 2021. "Characterizing Impacts of Storage Faults on HPC Applications: A methodology and insights." In IEEE International Conference on Cluster Computing (CLUSTER 2021), September 7-10, 2021, Portland, OR, 409-420. Los Alamitos, California:IEEE Computer Society. PNNL-SA-162815. doi:10.1109/Cluster48925.2021.00048
• Li A., B. Fang, C.E. Granade, G. Prawiroatmodjo, B. Heim, M. Roetteler, and S. Krishnamoorthy. 2021. "SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits." In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), November 14-19, 2021, Virtual, Online, Art. No. 97. New York, New York:Association for Computing Machinery. PNNL-SA-161181. doi:10.1145/3458817.3476169
• Stein S.A., B. Baheri, D. Chen, Y. Mao, Q. Guan, A. Li, and B. Fang, et al. 2021. "QuGAN: A Quantum State Fidelity based Generative Adversarial Network." In IEEE International Conference on Quantum Computing and Engineering (QCE 2021), October 17-22, 2021, Broomfield, CO, edited by H.A. Müller, et al, 71-81. Piscataway, New Jersey:IEEE. PNNL-SA-156090. doi:10.1109/QCE52317.2021.00023
• Stein S.A., R. L'Abbate, W. Mu, Y. Liu, B. Baheri, Y. Mao, and Q. Guan, et al. 2021. "A Hybrid System for Learning Classical Data in Quantum States." In IEEE International Performance Computing and Communications Conference (IPCCC 2021), October 29-31, 2021, Austin, TX, 1-7. Piscataway, New Jersey:IEEE. PNNL-SA-165589. doi:10.1109/IPCCC51483.2021.9679430

2020

• Geng T., C. Wu, C. Tan, B. Fang, A. Li, and M. Herbordt. 2020. "CQNN: a CGRA-based QNN Framework." In IEEE High Performance Extreme Computing Conference (HPEC 2020), September 22-24, 2020, Waltham, MA, 1-7. Piscataway, New Jersey:IEEE. PNNL-SA-153940. doi:10.1109/HPEC43674.2020.9286194

2019

• Fang B., H. Halawa, K. Pattabiram, M. Ripeanu, and S. Krishnamoorthy. 2019. "BonVoision: Leveraging Spatial Data Smoothness For Recovery From Memory Soft Errors." In Proceedings of the ACM International Conference on Supercomputing (ICS 2019), June 26-28, 2019, Phoenix, AZ, 484-496. New York, New York:ACM. PNNL-SA-143140. doi:10.1145/3330345.3330388