Associate Professor
Institute of Software,
Chinese Academy of Sciences (ISCAS)
Research Areas
My main research interests have been along three lines: (1) building high-performance and scalable systems for large-scale analytics; (2) exploring emerging software-hardware co-design technologies on new architectures; (3) coping with modern computing challenges that arise in data center, AI, intelligent edge and new HPC environments. These work for new system and software techniques include, not only implementing system-level optimizations (e.g., accelerated frameworks on GPUs, storage and memory management, and computation evolution), but also designing adaptive parallel paradigms for the different behaviors of algorithms and applications. In addition, I have taken on several projects in designing traditional software stacks, building high-performance libraries and systems, developing one-stop big-data platforms.
Currently, I'm working on high-performance OS on heterogeneous architectures (GPU, DPU, FPGA), distributed machine learning frameworks, robotic operating system and persistent memory techniques.
Previously, I got my PhD Degree from Institute of Software, Chinese Academy of Sciences in Jan. 2018.
Education
Sept. 2012 – Mar. 2018 Institute of Software, Chinese Academy of Sciences, Beijing, China
Ph.D student in Computer Software and Theory
Sept. 2008 – Jun. 2012 Northeastern University
B.S. in Computer Software Engineering
Experience
Work Experience
May 2018 – Now, Institute of Software, Chinese Academy of Sciences (ISCAS), Beijing, China
Associate Research Professor, focus on high perofmance computing / systems / machine learning
Publications
Papers
[1] Heng Zhang, Lingda Li, Hang Liu, Dongling Zhuang, Rui Liu, Chengyin Huan, Charles He, Yongchao Liu, Shuang Song, Dingwen Tao, Yanjun Wu, Shuaiwen Song. Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator System [C]. ACM International Conference on Supercomputing (ICS), 2022. ICS’22.
[2] Chengying Huan, Shuaiwen Leon Song, Yongchao Liu, Heng Zhang, Hang Liu, Charles He, Kang Chen, Jinlei Jiang, Yongwei Wu. T-GCN: A Sampling Based Streaming Graph Neural Network System With Hybrid Architecture [C]. 31st International Conference on Parallel Architectures and Compilation Techniques (PACT 2022).
[3] Heng Zhang, Lingda Li, Dongling Zhuang, Rui Liu, Shuang Song, Dingwen Tao, Yanjun Wu, Shuaiwen Song. An Efficient Uncertain Graph Processing Framework for Heterogeneous Architectures[C]. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2021. PPoPP’21.
[4] Pengpeng Hou, Heng Zhang, Yanjun Wu, Jiageng You, Yang He, Yuxia Miao. FindCmd: A Personalized Command Retrieval Tool[J]. IET Software, 2021.
[5] Hongjun Zhang, Heng Zhang, Libo Zhang, Yanjun Wu. FastUDP: A highly scalable user-level UDP framework in multi-core systems for fast packet I/O[J]. The Journal of Supercomputing, 2020, 11(3).
[6] Hongjun Zhang, Yanjun Wu, Heng Zhang, Libo Zhang. Hybrid access cache indexing framework adapted to GPU. International Journal of Software and Informatics, 2021, 11(2)
[7] Heng Zhang, Libo Zhang, Da Cheng, Yanjun Wu, Chen Zhao. EpCom: A parallel community detection approach for epidemic diffusion over social networks[C]. 2017 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2017: 1607-1614. BIBM 2017.
[8] Heng Zhang, Haibo Hou, Libo Zhang, Hongjun Zhang, Yanjun Wu. Accelerating Core Decomposition in Large Temporal Networks Using GPUs[C]. International Conference on Neural Information Processing. Springer, Cham, 2017: 893-903. ICONIP’17.
[9] Heng Zhang, Chunliang Hao, Yanjun Wu, Mingshu Li. Towards a scalable and energy-efficient resource manager for coupling cluster computing with distributed embedded computing[J]. Cluster Computing, 2017, 20(4): 3707-3720. Cluster Computing, 2017.
[10] Heng Zhang, Chunliang Hao, Yanjun Wu, Mingshu Li. Macaca: a scalable and energy-efficient platform for coupling cloud computing with distributed embedded computing[C]. IEEE Parallel and Distributed Processing Symposium Workshops. IEEE, 2016: 1785-1788. IPDRM’16, IPDPS 2016 Workshop.
[11] Chunliang Hao, Jie Shen, Celia Chen, Heng Zhang, Yanjun Wu, Mingshu Li. PCSsampler: Sample-based, Private-state Cluster Scheduling[C]. Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE Press, 2017: 599-608. CCGrid 2017.
[12] Chunliang Hao, Jie Shen, Heng Zhang, Xiao Zhang, Yanjun Wu and Mingshu Li. Tiresias: low-overhead sample based scheduling with task hopping[C]. IEEE International Conference on Cluster Computing. IEEE, 2016: 251-254. IEEE Cluster 2016.
[13] Chunliang Hao, Jie Shen, Heng Zhang, Xiao Zhang, Yanjun Wu and Mingshu Li. Sparkle: adaptive sample based scheduling for cluster computing[C]. Proceedings of the 5th International Workshop on Cloud Data and Platforms. ACM, 2015: 5. CloudDP 2015, Eurosys 2015
[14] Kai Wang, Heng Zhang, Yanjun Wu, Chen Zhao, Mingshu Li. Frog: A Distributed Graph Processing Engine from Sequential Subgraph Blocks. Work-in-Progress, ACM SOSP 2013.
[15] Yanjun Wu, Mingshu Li, Heng Zhang, Chunliang Hao. DataOS: A Data-centric Operating System for Future Data Center. Poster in EuroSys 2016.
Research Interests
High Performance Computing
Operating System
Distributed and Parallel System
Machine Learning
Collaboration
Jan. 2021-Jan. 2022, University of Sydney, Austrilia
Research Visting Scholar, FSA Lab (Professor Shuaiwen Leon Song), University of Sydney, Austrilia
• Work on high performance system building and big-data analytics.
Nov. 2014 – Jun. 2015, Intel Lab China, Beijing
Research Internship, Data Infrastructure Laboratory (DIL) in Intel Lab China, Intel Inc.
• Work on distributed communication framework optimization, e.g., Petuum system, Memcached, etc.
Nov. 2011 – Apr. 2012, Baidu Inc., Beijing
Software Engineering Internship, Baidu Map Team
• Responsible for launching webmap 2.0 online with team and webmap API v1.3 maintenance.
Students
现指导学生
代培元 硕士研究生 085405-软件工程
董岩松 硕士研究生 085405-软件工程
王上 硕士研究生 085405-软件工程