Research Interests

• Machine learning systems
• High-performance and data-intensive computing
• Parallel programming models

Selected Publication

• Improving Accuracy and Efficiency of Graph Embedding Training with Fine-grained Parameter Management. Lihan Hu and Peng Jiang. IPDPS 2025
• cuKE: An Efficient Code Generator for Score Function Computation in Knowledge Graph Embedding. Lihan Hu, Jing Li, and Peng Jiang. IPDPS 2024
• STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimization. Yihua Wei, Peng Jiang. SC 2022
• Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training. Peng Jiang, Lihan Hu and Shihui Song. NeurIPS 2022
• Rethinking Graph Data Placement for Graph Neural Network Training on Multiple GPUs. Shihui Song, Peng Jiang. ICS 2022
• A Novel Data Transformation and Execution Strategy for Accelerating Sparse Matrix Multiplication on GPUs. Peng Jiang, Changwan Hong, Gagan Agrawal. PPoPP 2020
• A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. Peng Jiang, Gagan Agrawal. NeurIPS 2018