Chenpeng Wu

Chenpeng Wu

Shanghai Jiao Tong University

I am currently a Ph.D. student at TCloud Lab, Shanghai Jiao Tong University (SJTU). I received my B.S. degree from Chien-Shiung Wu College, Southeast University.

Currently, my research is situated at the intersection of AI Infrastructure and High-Performance Computing (HPC). I am particularly interested in architecting efficient systems that scale deep learning workloads and optimizing performance for next-generation hardware. My goal is to bridge the gap between complex AI algorithms and the underlying hardware capabilities to enable more powerful and sustainable computing.

📝 Selected Publications

  • Eurosys 2025 Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores, Chenpeng Wu*, Qiqi Gu*, Heng Shi, et al.
    • Summary: This work addresses the compute and memory bottlenecks of MoE LLM inference by jointly exploiting structured sparsity in both activations and weights. It introduces a dedicated sparse data format and sparse-sparse kernels tailored for Sparse Tensor Cores, together with system-level optimizations for practical deployment.
  • PPoPP 2026 SPIDER: Unleashing Sparse Tensor Cores for Stencil Computation via Strided Swapping, Qiqi Gu*, Chenpeng Wu*, Heng Shi, et al.
    • Summary: This paper targets redundant computation caused by zero-padding when mapping stencil workloads to Tensor Cores. SPIDER proposes a strided-swapping transformation (offline kernel transformation plus online input reordering) to satisfy 2:4 structured sparsity constraints and effectively enable Sparse Tensor Cores for stencil acceleration.
  • TBD Do We Need Tensor Cores for Stencil Computations?, Qiqi Gu*, Chenpeng Wu*, Heng Shi, et al.
    • Summary: This study provides a systematic performance-modeling perspective on when stencil computations should use Tensor Cores. It quantifies transformation overhead and arithmetic-intensity shifts under temporal fusion, derives analytical suitability criteria, and explains how Sparse Tensor Cores broaden the range of stencil workloads that can benefit.

* Equal contribution / Joint first authors.

📖 Educations

  • 2021.09 - (now), Pursuing Ph.D, Shanghai Jiao Tong University.
  • 2017.09 - 2021.06, Chien-Shiung Wu College, Southeast University.