Zhen Xie

I am a researcher in the Data Science group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory, supervised by Dr. Murali Emani. My current research interests mainly focus on mixed precision optimization for the next-generation AI-accelerator systems.

Prior, I was a postdoctoral researcher in the Electrical Engineering and Computer Science at the University of California Merced, working with Prof. Dong Li on building efficient system supports for HPC and AI/DL workloads on persistent memory and GPU platforms. I obtained my Ph.D. degree at the Institute of Computing Technology of the Chinese Academy of Sciences (ICT, CAS), Beijing, China, under the guidance of Prof. Ninghui Sun and Prof. Guangming Tan. I received the B.S. degree in Computer Science and Technology at Wuhan University of Technology.

For more information, please click here for the Curriculum Vitae

Contact: zhen.xie(at)anl(dot)gov

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Research Interests
• Performance optimization on HPC and AI/DL applications • Parallel computing on various architecture
• Heterogeneous computing and memory systems • Scientific machine learning
News
  • [12/2021] A poster “An End-to-End Performance-Oriented Data Migration on Heterogeneous Memory” is accepted by PPoPP'22.
  • [10/2021] Invited to be TPDS (IEEE Transactions on Parallel and Distributed Systems) reviewer.
  • [8/2021] A paper “Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors” is accepted by SEC'21.
  • [8/2021] I will start my journey at ANL!
  • [8/2021] Invited talk at ANL: "Performance Optimization of ML and HPC Applications on Heterogeneous Systems"
  • [6/2021] A paper “A Pattern Based SpGEMM Library for Multi-core and Many-core Architectures” is accepted by TPDS.
  • [3/2021] I became a Arctic Code Vault Contributor in GitHub.
  • [1/2021] A paper “MD-HM: Memoization-based Molecular Dynamics Simulations on Big Memory System” is accepted in ICS'21.
  • [1/2021] A paper “Enabling Energy-Efficient DNN Training on Hybrid GPU-FPGA Accelerators” is accepted in ICS'21.
  • [1/2021] A paper “Tahoe: Tree Structure-Aware High Performance Inference Engine for Decision Tree Ensemble on GPU” is accepted in EuroSys'21.
  • [9/2020] A paper “Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation” is accepted in SC'20.
  • [8/2020] Got the renewed offer from UC Merced.
  • [3/2020] A paper, "RIANN: Real-time Incremental Learning with Approximate Nearest Neighbor on Mobile Devices", is accepted in OpML 2020.
  • [2/2020] A paper, "Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors", is accepted in MLSys-W 2020.
  • [8/2019] I will start my post-doctoral tour at UC Merced!
  • [7/2019] Invited talk at China University Of Petroleum: "Performance Prediction and Optimization of Floating Point Operating Patterns"
  • [6/2019] A paper “Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation” is accepted in SC'19.
  • [6/2019] I got my Ph.D degree at ICT, CAS.
  • Selected Publications
  • [SEC'21] Jie Liu, Jiawen Liu, Zhen Xie, and Dong Li, "Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors." ACM/IEEE Symposium on Edge Computing (SEC), 2021.
  • [TPDS'21] Zhen Xie, Guangming Tan, Weifeng Liu and Ninghui Sun, "A Pattern Based SpGEMM Library for Multi-core and Many-core Architectures." IEEE Transactions on Parallel and Distributed Systems (TPDS), 2021. Paper
  • [ICS'21] Zhen Xie, Wenqian Dong, Jie Liu, Ivy Peng, Yanbao Ma, and Dong Li. MD-HM: Memoization-based Molecular Dynamics Simulations on Big Memory System. ACM 35th International Conference on Supercomputing, 2021. (38/157=24.2%) Paper
  • [ICS'21] Xin He, Jiawen Liu, Zhen Xie, Hao Chen, Guoyang Chen, Weifeng Zhang, and Dong Li. Enabling Energy-Efficient DNN Training on Hybrid GPU-FPGA Accelerators. ACM 35th International Conference on Supercomputing, 2021. (38/157=24.2%) Paper
  • [EuroSys'21] Zhen Xie, Wenqian Dong, Jiawen Liu, Hang Liu and Dong Li. Tahoe: Tree Structure-Aware High Performance Inference Engine for Decision Tree Ensemble on GPU. ACM 16th European Conference on Computer Systems, 2021. (38/191=19.9%) Paper Slides Video
  • [SC'20] Wenqian Dong, Zhen Xie, Gokcen Kestor and Dong Li, Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation. International Conference for High Performance Computing, Networking, Storage and Analysis, 2020. (95/378=25.1%) Paper
  • [USENIX OpML'20] Jiawen Liu, Zhen Xie, Dimitrios Nikolopoulos, and Dong Li, "RIANN: Real-time Incremental Learning with Approximate Nearest Neighbor on Mobile Devices", USENIX Conference on Operational Machine Learning, 2020. Paper
  • [MLSys-W'20] Jiawen Liu, Jie Liu, Zhen Xie, and Dong Li, "Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors", On-Device Intelligence Workshop at Machine Learning and Systems Conference, 2020. Paper
  • [ICS'19] Zhen Xie, Guangming Tan, Weifeng Liu and Ninghui Sun, "IA-SpGEMM: an Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication." ACM 33rd on International Conference on Supercomputing, 2019. (45/193=23.3%) Paper Slides
  • [SC'19] Wenqian Dong, Jie Liu, Zhen Xie, and Dong Li, "Adaptive neural network-based approximation to accelerate eulerian fluid simulation." International Conference for High Performance Computing, Networking, Storage and Analysis, 2019. (87/344=25.3%) Paper
  • [ICPADS'16] Zhen Xie, Zheng Cao, Zhan Wang, Dawei Zang, En Shao and Ninghui Sun, "Modeling Traffic of Big Data Platform for Large Scale Datacenter Networks," IEEE 22nd International Conference on Parallel and Distributed Systems, 2016. (123/412=29.9%) Paper
  • Zhen Xie, Guangming Tan and Ninghui Sun, "PRF : A Process-RAM-Feedback Performance Model to Reveal Bottlenecks and Propose Optimizations." High Technology Letters, 2019. Paper
  • Zhen Xie, Guangming Tan and Ninghui Sun, Revealing bottlenecks and predicting optimal performance of Sparse Matrix-Vector and Convolution using the Probability-Process-Ram model, Computer Research and Development, 2020. Paper
  • Professional Services
  • Reviewers: TPDS, TECS, LCTES'21, ICS'21, IPDPS'21, IPDPS'20, NPC'20, IPDPS’19, ICPP’19, PPOPP’19, Cluster’19, NPC’19, SC’18, CCGrid’17, etc.
  • Last updated on Dec, 2021.

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