Feng Li

I am Feng Li, currently a PhD student Purdue University, Indianapolis (IUPUI campus). I work with my advisor Dr. Fengguang Song in areas of Distributed Systems and High Performance Computing.

Contact Me

You can reach me through lifen at iupui dot edu or li2251 at purdue dot edu.

My lab is located at: 719 Indiana Ave WK300, Indianapolis, IN 46222.


  • [2015-~] Indiana University-Purdue University, Indianapolis, IN

    PhD student, Computer Science.

  • [2011~2015] Huazhong University of Science & Technology, Wuhan, China

    Bachelor in Computer Science and Technology.

Research Projects

Computer Science Department at IUPUI

Jan 2019~ , Architucture-aware Neural Network

Optimize Neural Networks from the system prospective:

  • Co-locate data in deep/heterogeneous memory hierarchy, so that communication overhead between different components can be minimized.

  • Intelligent scheduling algorithm for computation kernels to enable efficient and large-scale parallelism in modern SIMD and NUMA architectures.

  • Work in progress.

Nov 2017~ Mar 2018, Memory-aware Lattice-Boltzmann Method

Prototype and optimize a memory-aware Lattice-Boltzmann Method (LBM, a computational fluid dynamics approach to simulating complex fluid flows), which can enhance data reuse across multiple time steps.

  • A paper was accepted by SBAC-PAD‘18.

April 2017~ Nov 2017, Performance Analysis of In-situ Methods in HPC

In-depth performance evaluation of various high-performance transport methods (ADIOS, flexpath, DataSpaces, Decaf, DIMEs) in multiple HPC systems.

  • A paper was accepted by HPDC‘18.

Aug 2016 ~ Mar 2017, Machine Learning Workflow in HPC

Working on s software framework for scientific workflows where RDMA technique is used to couple numerical simulation, data analysis and real-time visualization application together.

  • A distributed and optimized anomaly detection method is used to detect vortex and other special patterns from turbulence flows.

  • A paper was accepted by PEARC‘17.

Aug 2015 ~ Apr 2016, KSSR

Working on a kernelized sparse self-representation model(KSSR) and a novel Kernelized Fast Iterative Soft-Thresholding Algorithm(K-FISTA), to recover the underlying nonlinear structure among data.

  • My work mainly includes the implementation, evaluation of the KSSR model and K-FISTA algorithm.

  • A paper was accepted in SDM‘16.

Storage system group at IBM Research, Almaden

May 2018~ Aug 2018, research internship

Working on a high-performance key-value store, which uses NVMe SSD as data storage and keeps critical metadata in the persistent memory (pmem). By utilizing the advantages of persistent memory, the access to metadata such as block allocation and key-value mappings can be both hardened and fast.

May 2017~ Aug 2017, research internship

Working on persistent memory management on NVMe devices. A “collaborative paging” service is designed for extended memory mode. Paging service is lifted to userspace, to avoid expensive context switches and lengthy I/O stacks in legacy kernel designs . DPDK/SPDK tools are used to enable fast access via user space.

  • A paper is accepted in HP3C‘19.


  • Feng Li, Daniel G. Waddington, Fengguang Song, Userland CO-PAGER: Boosting Data-Intensive Applications with Non-volatile Memory, accepted in Proceedings of the 3rd International Conference on High Performance Compilation, Computing and Communications (HP3C‘19), Xian, China. ACM, 2019.

  • Yuankun Fu, Feng Li, Fengguang Song, Luoding Zhu, Designing a Parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems, Proceedings of 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD‘18), Lyon, France. 2018. pdf, bibtex

  • Feng Li, Fengguang Song, Building a scientific workflow framework to enable real‐time machine learning and visualization, Concurrency and Computation: Practice and Experience (CCPE). Wiley, 2018. pdf, bibtex

  • Yuankun Fu, Feng Li, “Fengguang Song, Performance Analysis and Optimization of In-situ Integration of Simulation with Data Analysis: Zipping Applications Up”. Proceedings of the 27th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC‘18). ACM, 2018. pdf, bibtex

  • Feng Li, and Fengguang Song. “A Real-Time Machine Learning and Visualization Framework for Scientific Workflows.” Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (PEARC‘17). ACM, 2017. (Best Student Paper Award) pdf, bibtex

  • Xiao Bian, Feng Li, Xia Ning, Kernelized Sparse Self-Representation for Clustering and Recommendation. In SIAM International Conference on Data Mining (SDM‘16). SIAM, 2016. pdf, bibtex

  • Luo, Dan, Jiguang Wan, Yifeng Zhu, Nannan Zhao, Feng Li, and Changsheng Xie, Design and Implementation of a Hybrid Shingled Write Disk System. In IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE, 2016. pdf, bibtex


  • My spare time

I like hiking, jogging. I have travelled to many great places in China, and I am planning to visit more places in the US! In my spare time I like playing pingpong or tennis with friends.