About Me

I am a PhD Candidate at University of California, Riverside. Prior to the PhD study in Computer Science, I obtained MS and BS degrees from Columbia University and Peking University.

News

  • Jun 2025: Three papers were accepted at SC’25
  • Mar 2025: Gave a talk at the Las Vegas, NV (PPoPP 2025).
  • June. 2023: I gave a talk at International Conference on Supercomputing 2023.
  • April. 2023: A paper was accepted at International Conference on Supercomputing 2023.

Education

  • Ph.D. in Computer Science (Sep. 2022 – Present)
    University of California, Riverside (UCR)
    Advisor: Prof. Zizhong Chen

  • M.S. in Electrical Engineering (Sep. 2020 – May 2022)
    Columbia University

  • B.S. in Computer Science (Sep. 2016 – Jul. 2020)
    B.S. in Economics (Double Major)
    Peking University


Research Experience

  1. USC ISI / Argonne National Laboratory (Jan. 2024 – Present)
    Los Angeles, CA / Lemont, IL
    Scientific Workflow Applications on Resilient Metasystem
    Mentors: Dr. Franck Cappello, Dr. Sheng Di, Dr. Krishnan Raghavan (ANL); Dr. Ewa Deelman (USC ISI)
    • Designed a Q-learning + GNN-based topology protocol (DGRO) that reduces network diameter by optimizing virtual rings over heterogeneous, failure-prone systems.
    • Implemented a single-hop gossip-based failure detector, resilient to network jitter and churn, enabling decentralized membership monitoring across 20+ globally distributed sites.
    • Deployed DGRO on the FABRIC testbed spanning Japan, Europe, Hawaii, and 15+ U.S. locations, demonstrating fast convergence and robustness at international scale.
  2. UCR / Lawrence Berkeley National Laboratory (Sep. 2022 – Present)
    Riverside, CA
    Data-driven Exascale Control of Optically Driven Excitations in Chemical and Material Systems
    Mentor: Dr. Zizhong Chen
    • Designed and implemented in-kernel ABFT GEMM using tensor cores, achieving higher performance than cuBLAS while ensuring fault detection and correction under soft errors.
    • Developed a fully GPU-resident ABFT FFT pipeline, outperforming cuFFT, and enabling error-resilient spectral analysis in scientific simulations.
    • Proposed the first ABFT-enabled K-means clustering framework on GPUs, exceeding cuML performance with integrated resilience support.
    • Innovated lightweight, low-overhead in-kernel fault tolerance mechanisms across linear algebra and ML workloads, demonstrating resilience-performance co-design in exascale systems.
  3. Nvidia (Jun. 2024 – Sep. 2024)
    Santa Clara, CA
    Compiler Optimization for OpenMP Target Offload on Heterogeneous GPU Architectures
    Mentor: Dr. David Appelhans
    • Investigated performance bottlenecks of OpenMP target offload in SPEChpc 2021 on GH200/H200 GPUs.
    • Developed compiler/runtime optimizations achieving up to 10× speedup without source code changes.
    • Analyzed OpenMP vs. OpenACC performance and contributed optimized versions to SPEChpc 1.1.9.
    • Work adopted by RWTH Aachen University, demonstrating both research impact and practical relevance.
  4. Columbia University / AI4Finance Foundation (Aug. 2021 – Jul. 2022)
    New York, NY
    ElegantRL: Massively Parallel Deep Reinforcement Learning Library
    Mentors: Dr. Xiaoyang Liu, Dr. Xiaodong Wang
    • Developed multi-agent RL algorithms in ElegantRL, a popular RL library with ~4k GitHub stars.
    • Co-led ElegantRL_Solver, a high-performance solver that outperforms Gurobi for dense MaxCut problems.

Selected Publications

Full list in Google Scholar

1.
SC '25
Boosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration. [paper] [code]
Shixun Wu*, Jinwen Pan*, Jinyang Liu, Jiannan Tian, Ziwei Qiu, Jiajun Huang, Kai Zhao, Xin Liang, Sheng Di, Zizhong Chen, and Franck Cappello.
2.
SC '25
TurboFNO: High-Performance Fourier Neural Operator with Fused FFT-GEMM-iFFT. [paper] [code]
Shixun Wu, Yujia Zhai, Huangliang Dai, Yue Zhu, Haiyang Hu, and Zizhong Chen.
3.
PPoPP '25
TurboFFT: Co-Designed High-Performance and Fault-Tolerant Fast Fourier Transform on GPUs. [paper] [code]
Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Sheng Di, Franck Cappello, Zizhong Chen.
4.
SC '24
cuSZ-I: High-Fidelity Error-Bounded Lossy Compression for Scientific Data on GPUs. [paper] [code]
Jinyang Liu*, Jiannan Tian*, Shixun Wu*, Sheng Di, Boyuan Zhang, Robert Underwood, Yafan Huang, Jiajun Huang, Kai Zhao, Guanpeng Li, Dingwen Tao, Zizhong Chen, Franck Cappello.
5.
ICS '23
Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs. [paper] [code]
Shixun Wu*, Yujia Zhai*, Jinyang Liu, Jiajun Huang, Zizhe Jian, Bryan Wong, Zizhong Chen.
6.
Cluster '24
FT K-means: A High-Performance K-means on GPU with Fault Tolerance. [paper] [code]
Shixun Wu*, Yitong Ding*, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Huangliang Dai, Sheng Di, Bryan Wong, Zizhong Chen, Franck Cappello.
7.
SC '25
FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention. [paper]
Huangliang Dai, Shixun Wu, Jiajun Huang, Zizhe Jian, Yue Zhu, Haiyang Hu, and Zizhong Chen.
8.
SIGMOD '24
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation. [paper]
Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Sian Jin, Zizhe Jian, Jiajun Huang, Shixun Wu, Zizhong Chen, Franck Cappello.
9.
IPDPS '24
CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction. [paper]
Zizhe Jian, Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Haiying Xu, Robert Underwood, Shixun Wu, Jiajun Huang, Zizhong Chen, and Franck Cappello.
10.
BigData '23
Exploring Wavelet Transform Usages for Error-bounded Scientific Data Compression. [paper]
Jiajun Huang, Jinyang Liu, Sheng Di, Yujia Zhai, Zizhe Jian, Shixun Wu, Kai Zhao, Zizhong Chen, Yanfei Guo, Franck Cappello.
11.
Allerton '23
Downlink beamforming optimization via deep learning. [paper]
Jeremy Johnston, Xiaoyang Liu, Shixun Wu, Xiaodong Wang.