Biography：
Wenli Zhang, Ph.D., senior engineer, senior member of China Computer Society (CCF), executive member of CCF high performance computing professional committee, member of IEEE/ACM, is currently the leader of the network group. She obtained a master and a doctor's degree in computer system architecture from the Institute of Computing Technology, Chinese Academy of Sciences, and participated in the research and development of Dawning 4000/5000/6000 high-performance computers as the key researcher, as well as the sea-cloud pilot project of Chinese Academy of Sciences. Recently, she has undertaken and participated in many projects, such as the national key R & D plan, the key projects of the National Natural Science Foundation, the strategic pilot special project of the Chinese Academy of Sciences, and the Huawei joint laboratory project. She has published dozens of paper, patents and some open source work, and has assisted to guide more than 20 doctoral and master students in total.
Research area：
computer architecture, mainly focuses on the research of network stack system structure and optimization at present
Selected papers：
1) Zhang W L, Liu K, Shen Y F, et al. Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices[J]. Journal of Computer Science and Technology, 2020, 35(1): 179-193.
2) Wu W Q, Feng X, Zhang W L, et al. MCC: A predictable and scalable massive client load generator[C]//International Symposium on Benchmarking, Measuring and Optimization. Springer, Cham, 2019: 319-331.
3) Song H, Zhang W L, Liu K, et al. HCMonitor: An accurate measurement system for high concurrent network services[J]. Concurrency and Computation: Practice and Experience, 2022, 34(12): e6081.
4) Huang Y B, Cui Z H, Chen L C, Zhang W L, et al. HaLock: Hardware-assisted lock contention detection in multithreaded applications[C]//Proceedings of the 21st international conference on Parallel architectures and compilation techniques. 2012: 253-262.
5) Sun Y F, Liang Y C, Zhang W L, et al. Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting[J]. Neural Computing & Applications, 2005, 14(1): 36-44.
