Feng Zhang     张 峰

Address: Room 427, Information Building, No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China.
Email: zh...@gmail.com

I am an assistant professor in Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China. My research interests include Parallel and Distributed Computing, Machine Learning, and Heterogeneous Database Systems.

Our group (DBIIR) is looking for undergraduate interns and graduate students. If you are interested in system research and parallel computing, please contact me.


Research Interests:

Parallel and Distributed Systems; Heterogeneous Database Systems; High Performance Computing

Professional Activities:

Publication Chair for NPC'18. PC Member for ICPADS'18. External Reviewer for SC'18. External Reviewer for ICS'18. External Reviewer for ICPP'18.

Reviewer for TPDS, Journal of Supercomputing.

Education Background:

1.      Sep. 2012 ~ Jul. 2017: PhD in Computer Science, Institute of High-Performance Computing, Department of Computer Science and Technology, Tsinghua University. Advisor: Prof. Wenguang Chen and Prof. Jidong Zhai.

2.      Sep. 2008 ~ Jul. 2012: B.S. in School of Computer Science and Technology, Xidian University.

Conference Papers:

[VLDB'18]       Feng Zhang, Jidong Zhai, Xipeng Shen, Onur Mutlu, and Wenguang Chen. Efficient Document Analytics on Compressed Data: Method, Challenges, Algorithms, Insights. The 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil, August 27-31, 2018.

[ICS'18]      Feng Zhang, Jidong Zhai,Xipeng Shen, Onur Mutlu, and Wenguang Chen. Zwift: A Programming Framework for High Performance Text Analytics on Compressed Data. The 32nd ACM International Conference on Supercomputing, Beijing, China, June 12-15, 2018.

[CGO'17]      Feng Zhang, Jidong Zhai, Wenguang Chen, Bingsheng He and Shuhao ZhangHe. FinePar: Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures, Proceedings of the 2017 International Symposium on Code Generation and Optimization. IEEE Press, 2017: 27-38.

[MASCOTS'15]      Feng Zhang, Jidong Zhai, Wenguang Chen, Bingsheng He and Shuhao Zhang, To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures. IEEE 23nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, October 5-7, 2015, Atlanta, GA, USA. [CODE]

[ICPPW'15]      Feng Zhang, and Liang Chen, HPC-Oriented Power Evaluation Method. IEEE 44th International Conference on Parallel Processing Workshops, September 1-4, 2015, Beijing, China.

[APSys’14]      Junli Gu, Maohua Zhu, Zhitao Zhou, Feng Zhang, Zhen Lin, Qianfeng Zhang, Mauricio Breternitz, Implementation and Evaluation of Deep Neural Networks (DNN) on Mainstream Heterogeneous Systems. Proceedings of 5th Asia-Pacific Workshop on Systems, ACM, June 25-26, 2014, Beijing, China.

Journal Papers:

[TPDS'17]      Feng Zhang, Jidong Zhai, Bingsheng He, Shuhao Zhang, Wenguang Chen, Understanding Co-running Behaviors on Integrated CPU/GPU Architectures,IEEE Transactions on Parallel and Distributed Systems, 2017, 28(3): 905-918. [CODE]

[SCIS'15]      ZHAI JiDong, ZHANG Feng, LI QingWen, CHEN WenGuang, ZHENG WeiMin, Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays. SCIENCE CHINA Information Sciences, Published: 19 April 2015.

Research and Development Experience:

1.      August 2013 - December 2013: Internship, Deep Neural Networks Research Group, AMD, Beijing, China.

2.      April 2016 - November 2016: Visiting Scholar, Compiler and System Research Group, NC State University, Raleigh, North Carolina, USA.

Recent Awards & Honors:

1.      “Guanghua Scholarship” of Tsinghua University ("清华之友-光华二等奖学金 综合三等", Third-Class Scholarship), Oct. 2013.

2.      “Guanghua Scholarship” of Tsinghua University ("清华之友-光华二等奖学金 综合三等", Third-Class Scholarship), Oct. 2014.

3.      “Guanghua Scholarship” of Tsinghua University ("清华之友-光华一等奖学金 综合二等", Second-Class Scholarship), Oct. 2015.

4.      “National Scholarship” ("国家奖学金"), Oct, 2016.

5.      “Beijing Outstanding Graduates” ("北京市优秀毕业生"), Jul, 2017.

6.      “Outstanding Graduates in Department of Computer Science, Tsinghua University” ("清华大学计算机系优秀毕业生"), Jul, 2017.


1.      MPMD 863 Project. A performance tool that can analyze MPI trace.

2.      China Grid 863 Project. HPC evaluation for large clusters in China.[WebLink]

3.      CoRunBench. Benchmark for Co-running Single Applications on Integrated Architectures.[WebLink]



      Programming Design (Summer 2018)

      Parallel Computing (Fall 2017)