Chen Wang

Contact: wang116@llnl.gov

I am currently a Fernbach Postdoctoral Fellow at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. I completed my Ph.D. in Computer Science at the University of Illinois Urbana-Champaign, under the guidance of Prof. Marc Snir. You can find my CV here.

My research interests include high-performance computing, high-performance storage systems, parallel I/O, and tracing and analysis. During my Ph.D. and postdoctoral research, I have extensively studied performance issues related to POSIX consistency and explored the design of next-generation high-performance storage systems. Many of the projects I have been involved in are centered around these areas.

Some of my ongoing projects:

• UnifyFS: A specialized burst buffer parallel file system for supercomputers. UnifyFS manages node-local fast storage devices (e.g., SSD and NVMe) and provides a unified view for applications. Applications can use UnifyFS just as they would traditional global file systems, requiring no code changes. Notably, UnifyFS was awarded the 2024 R&D 100 Award!

• Recorder: A comprehensive parallel I/O and tracing library, initially developed for my study on HPC I/O characterization and storage system consistency. Recorder collects detailed information about I/O calls, MPI calls, and their parameters, enabling analysis that existing tracing tools could not provide. It captures near-lossless information across the entire HPC I/O software stack, including POSIX, MPI-IO, HDF5, NetCDF, and PnetCDF.

• Pilgrim: Pilgrim traces all MPI calls (over 400 functions as per MPI Standard 4.0) along with their parameters and compresses the trace online using a novel pattern-recognition-based algorithm. Compared to state-of-the-art MPI tracing tools, Pilgrim captures more information while using less storage. Due to time constraints, I have merged most of Pilgrim’s features into Recorder and stopped maintaining Pilgrim. You can now use Recorder for both MPI and I/O tracing.

• DYAD: DYAD is a data streamer optimized for producer-consumer workloads, enabling consumers to retrieve data directly from producers via RDMA, bypassing costly file system interactions. Like UnifyFS, DYAD can utilize node-local storage devices and has demonstrated significant improvements in I/O bandwidth for deep learning training applications.

• VerifyIO: VerifyIO addresses the challenges posed by user-level parallel file systems that deviate from POSIX semantics to improve performance. These systems often define their consistency semantics informally, complicating correctness verification for applications. VerifyIO provides a formal framework for specifying storage semantics and uses a trace-driven approach (leveraging Recorder traces) to verify correctness.

• MPI Standard Revision: While working on the VerifyIO project, I uncovered several consistency issues in production-level I/O libraries. Further investigations revealed that these issues stemmed from the strict specifications of the MPI Standard, particularly regarding the MPI_File_sync call. To address these limitations, I am leading an effort within the MPI-I/O working group to revise the MPI Standard, aligning it more closely with the needs of modern I/O libraries and next-generation storage systems.

Publications:

Peer-reviewed papers and journal articles are listed below. A complete list of my publications is available in my CV.

2024:

• Hariharan Devarajan, Ian Lumsden, Chen Wang, Konstantia Georgouli, Jae-Seung Yeom, and Michela Taufer. DLDM: Locality-aware Data Management for accelerating Deep Learning Training. IEEE/SBC 36th International Symposium on Computer Architecture and High Performance Computing. SBAC-PAD. 2024.
• Chen Wang, Houjun Tang, Jean Luca Bez and Suren Byna. Object-Centric Data Management in HPC Workflows - A Case Study. 4th Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads. 2024.
• Olga Kogiou, Hariharan Devarajan, Chen Wang, Weikuan Yu and Kathryn Mohror. Understanding Adaptable Storage for Diverse Workloads. 4th Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads. 2024.
• Chen Wang, Kathryn Mohror, and Marc Snir. “Formal Definitions and Performance Comparison of Consistency Models for Parallel File Systems”, IEEE TPDS. 2024.

2022:

• Chen Wang, Yanfei Guo, Pavan Balaji, and Marc Snir. “Near-Lossless MPI Tracing and Proxy Application Autogeneration”, IEEE TPDS, 2022.

2021:

• Chen Wang, Pavan Balaji, and Marc Snir. “Pilgrim: Scalable and (near) Lossless MPI Tracing”, SC, 2021.
• Sushma Yellapragada, Chen Wang, and Marc Snir. “Verifying IO Synchronization from MPI Traces”, PDSW, 2021.
• Chen Wang, Kathryn Mohror, and Marc Snir. “File System Semantics Requirements of HPC Applications”, HPDC, 2021.

2020:

• Jinghan Sun, Chen Wang, Jian Huang, and Marc Snir. “Understanding and Finding Crash-Consistency Bugs in Parallel File Systems” 12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), 2020.
• Chen Wang, Jinghan Sun, Marc Snir, Kathryn Mohror, and Elsa Gonsiorowski. “Recorder 2.0: Efficient Parallel I/O Tracing and Analysis”, IEEE International Workshop on High-Performance Storage (HPS), 2020.

2019:

• Na Bai, Shanjiang Tang, Ce Yu, Hao Fu, Chen Wang, and Xi Chen. “GMSA: a data sharing system for multiple sequence alignment across multiple users”, Current Bioinformatics 14, no. 6 (2019): 504-515.

2018:

• Chen Wang, Nikoli Dryden, Franck Cappello, and Marc Snir. “Neural network based silent error detector”, 2018 IEEE International Conference on Cluster Computing (CLUSTER, Best Paper), pp. 168-178. IEEE, 2018.

2017:

• Xi Chen, Chen Wang, Shanjiang Tang, Ce Yu, and Quan Zou. “CMSA: a heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment”, BMC bioinformatics 18, no. 1 (2017): 315.

2016:

• Chen Wang, Ce Yu, Shanjiang Tang, Jian Xiao, Jizhou Sun, and Xiangfei Meng. “A general and fast distributed system for large-scale dynamic programming applications”, Parallel Computing 60 (2016): 1-21.

2015:

• Chen Wang, Ce Yu, Jizhou Sun, and Xiangfei Meng. “DPX10: An efficient X10 framework for dynamic programming applications”, In 2015 44th International Conference on Parallel Processing (ICPP), pp. 869-878. IEEE, 2015.

Community Service:

• Program Chair: ESSA'25
• Publicity Chair: SSDBM'25, ESSA'2025
• Workshop Review Committee: ISC'25
• Program Committee: IPDPS'25, ICS'25, SC'24, CLUSTER'24, CLUSTER'25, PERMAVOST'24, ESSA'23, PDSW'23, CHEOPS'23, REX-IO'23
• Journal Reviewer Board: IEEE TPDS