Wanling Gao

Last modified: 2021/11/3
Wanling Gao (高婉铃)

Associate Professor
Institute of Computing Technology (ICT)
Chinese Academy of Sciences (CAS)


Google Scholar
   Mailing Address:
    No.6 Kexueyuan South Road,
    Haidian District,
    Beijing, China


I am an associate professor at the Institute of Computing Technology, Chinese Academy of Sciences since 2021 and an assistant professor from 2019 to 2021. My research focuses on big data and AI benchmarking, workload characterization, computer architecture, and proxy benchmarks for simulation. I received my Ph.D. degree in 2019 from Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences. Prof. Jianfeng Zhan is my advisor. I received my B.S. degree in 2012 from Huazhong University of Science and Technology in China.


My research interests focus on the following points of Data center computing and benchmarking:
  • Big data and AI benchmarking.
  • Workload Characterization.
  • Computer Architecture.
  • Profiling and tracing in data centers.
  • Proxy benchmarks for simulation.
Undergoing projects
  • ScenarioBench: Modeling, Characterizing, and Optimizing Ultra-scale Real-world or Future Applications and Systems Using Benchmarks. We propose a scenario-distilling methodology that formalizes a real-world or future application scenario as a Directed Acyclic Graph-based model (in short, DAG model). We design and implement a reusing benchmark framework. Based on the framework, we implement the scenario benchmarks---AIBench Scenario, including E-commerce Search Intelligence and Online Translation Intelligence. AIBench Scenario is a benchmarks suite modeling AI-augmented Internet service scenarios, including E-commerce Search Intelligence and Online Translation Intelligence. Each scenario benchmark models the critical paths of a real-world application scenario as a permutation of the AI and non-AI modules.
  • AIBench Training: Balanced AI Training Benchmarks. We present a balanced methodology considering comprehensiveness, representativeness, affordability, and portability. Our methodology widely investigates AI tasks and models and covers the algorithm-level, system-level, and microarchitectural-level factors space to the most considerable extent. We provide nineteen real-world AI workloads to achieve comprehensiveness and representativeness. Besides, we propose two AIBench Training subset: RPR and WC subsets to achieve affordability. We give the hotspot functions as microbenchmarks (AIBench Micro) to achieve portability for simulator-based research after profiling.
  • AIBench Inference: Comprehensive AI Inference Benchmarks. We present a comprehensive AI inference benchmark suite including nineteen real-world AI workloads and covering text processing, image processing, audio processing, video processing, and 3D data processing.
  • HPC AI500: A Benchmark Suite for HPC AI Systems. HPC AI500 presents a comprehensive methodology, tools, Roofline performance models, and innovative metrics for benchmarking, optimizing, and ranking HPC AI systems. We abstract the HPC AI system into nine independent layers, and present explicit benchmarking rules and procedures to assure equivalence of each layer, repeatability, and replicability.
  • Data Motifs: A Lens towards Fully Understanding Big Data and AI Workloads. Identifying abstractions of time-consuming units of computation is an important step toward domain-specific hardware and software co-design. Straightforwardly, we can tailor the architecture to characteristics of an application, several applications, or even a domain of applications. Data motif is a new approach to modelling and characterizing big data and AI workloads. We consider each big data and AI workload as a pipeline of one or more classes of unit of computation performed on different initial or intermediate data inputs, each of which captures the common requirements while being reasonably divorced from individual implementations. We call this abstraction a data motif. Significantly different from the traditional kernels, a data motif’s behaviors are affected by the sizes, patterns, types, and sources of different data inputs; Moreover, it reflects not only computation patterns, memory access patterns, but also disk and network I/O patterns.
Past projects
  • BigDataBench 4.0 project. BigDataBench 4.0 adopts a scalable data motif based benchmarking methodology and contains 13 representative real-world data sets and 47 benchmarks. The latest version of BigDataBench is available from http://www.benchcouncil.org/BigDataBench/index.html.
  • Proxy Benchmark Generating for Big Data and AI. We propose a data motif-based proxy benchmark generating methodology which combines data motifs with different weights to mimic the big data and AI workloads. The proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures.
  • High Throughput Computers project, Institute of Computing Technology, Chinese Academy of Sciences, Beijing. System and micro-architectural level analysis of high throughput workloads.


  • AIBench Scenario: Scenario-distilling AI Benchmarking. [pdf].
    Wanling Gao, Fei Tang, Jianfeng Zhan, Xu Wen, Lei Wang, Zheng Cao, Chuanxin Lan, Chunjie Luo, Xiaoli Liu, Zihan Jiang.
    The 30th International Conference on Parallel Architectures and Compilation Techniques (PACT 2021).
  • AIBench Training: Balanced Industry-Standard AI Training Benchmarking. [pdf].
    Fei Tang, Wanling Gao, Jianfeng Zhan, Chuanxin Lan, Xu Wen, Lei Wang, Chunjie Luo, Zheng Cao, Xiongwang Xiong, Zihan Jiang, Tianshu Hao, Fanda Fan, Fan Zhang, Yunyou Huang, Jianan Chen, Mengjia Du, Rui Ren, Chen Zheng, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Jiahui Dai, Hainan Ye.
    2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2021).
  • HPC AI500 V2.0: The Methodology, Tools, and Metrics for Benchmarking HPC AI Systems. [pdf].
    Zihan Jiang, Wanling Gao, Fei Tang, Lei Wang, Xingwang Xiong, Chunjie Luo, Chuanxin Lan, Hongxiao Li, Jianfeng Zhan.
    2021 IEEE International Conference on Cluster Computing (CLUSTER 2021).
  • WPC: Whole-Picture Workload Characterization Across Intermediate Representation, ISA, and Microarchitecture. [pdf].
    Lei Wang, Xingwang Xiong, Jianfeng Zhan, Wanling Gao, et al.
    IEEE Computer Architecture Letters (2021).
  • AI-oriented Workload Allocation for Cloud-Edge Computing. [pdf].
    Tianshu Hao, Jianfeng Zhan, Kai Hwang, Wanling Gao, Xu Wen.
    The 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021).
  • AIBench: Towards Scalable and Comprehensive Datacenter AI Benchmarking. [pdf].
    Wanling Gao, Chunjie Luo, Lei Wang, Xingwang Xiong, Jianan Chen, Tianshu Hao, Zihan Jiang, Fanda Fan, Mengjia Du, Yunyou Huang, Fan Zhang, Xu Wen, Chen Zheng, Xiwen He, Jiahui Dai, Hainan Ye, Zheng Cao, Zhen Jia, Kent Zhan, Haoning Tang, Daoyi Zheng, Biwei Xie, Wei Li, Xiaoyu Wang, and Jianfeng Zhan.
    2018 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench18).
  • Data Motifs: A Lens Towards Fully Understanding Big Data and AI Workloads. [pdf].
    Wanling Gao, Jianfeng Zhan, Lei Wang, Chunjie Luo, Daoyi Zheng, Fei Tang, Biwei Xie, Chen Zheng, Xu Wen, Xiwen He, Hainan Ye, Rui Ren.
    The 27th International Conference on Parallel Architectures and Compilation Techniques (PACT18).
  • Data Motif-based Proxy Benchmarks for Big Data and AI Workloads. [pdf].
    Wanling Gao, Jianfeng Zhan, Lei Wang, Chunjie Luo, Zhen Jia, et al.
    IEEE International Symposium on Workload Characterization (IISWC 2018).
  • BigDataBench: a Data Motif-based Big Data and AI Benchmark Suite [pdf].
    Wanling Gao, Jianfeng Zhan, Lei Wang, Chunjie Luo, et al.
    arXiv preprint arXiv:1802.08254.
  • BigDataBench: a Big Data Benchmark Suite from Web Search Engines [pdf].
    Wanling Gao, Yuqing Zhu, Zhen Jia, Chunjie Luo, Lei Wang, Jianfeng Zhan, et al.
    Third Workshop on Architectures and Systems for Big Data (ASBD 2013) in conjunction with The 40th International Symposium on Computer Architecture, May 2013.
  • BigDataBench:An Open Source Big Data Benchmark Suite [pdf].
    Jianfeng Zhan, Wanling Gao, Lei Wangi, et al.
  • Landscape of big medical data: a pragmatic survey on prioritized tasks. [pdf].
    Zhifei Zhang, Wanling Gao, Fan Zhang, et al.
    IEEE Access, 2019.
  • Understanding Processors Design Decisions for Data Analytics in Homogeneous Data Centers. [pdf].
    Zhen Jia, Jianfeng Zhan, Lei Wang, Chunjie Luo, Wanling Gao, Yi Jin, Rui Han and Lixin Zhang.
    IEEE Transactions on Big Data, 2017.
  • CVR: Efficient Vectorization of SpMV on X86 Processors. [pdf].
    Biwei Xie, Jianfeng Zhan, Xu Liu, Wanling Gao, Zhen Jia, Xiwen He, and Lixin Zhang.
    CGO 2018.
  • BigDataBench: a Big Data Benchmark Suite from Internet Services. [pdf].
    Lei Wang, Jianfeng Zhan, Chunjie Luo, Yuqing Zhu, Qiang Yang, Yongqiang He, Wanling Gao, Zhen Jia, Yingjie Shi, Shujie Zhang, Cheng Zhen et al.
    HPCA 2014, Industry Session.
  • Understanding Big Data Analytics Workloads on Modern Processors. [pdf].
    Zhen Jia, Jianfeng Zhan, Lei Wang, Chunjie Luo, Wanling Gao, Yi Jin, Rui Han, et al.
    TPDS 2016.
  • Trends on Methods for Prediction of Tandem Mass Spectra of Peptides. [pdf].
    Xiexuan Zhou, Rui Ren, Wanling Gao, Yunyou Huang, Wenfeng Zeng, Defei Kong, Tianshu Hao, Zhifei Zhang, and Jianfeng Zhan.
    Progress in Biochemistry and Biophysics, 2018.

Invited Talks & Tutorials

Invited Talks

Professional Activities


  • 2021.11 2021 BenchCouncil Rising Star Award
  • 2019.1 Outstanding Graduates of UNIVERSITY OF CHINESE ACADEMY OF SCIENCE
  • 2017.10 National Scholarship for Ph.D.
  • 2015.12 Doctoral scholarship, ICT, CAS, Beijing

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