Research Interests:

• Compiler and runtime support for high-performance machine learning
• Parallelization for data-intensive and compute-intensive applications
• Programming models for irregular applications

Selected Publication:

• Peng Jiang, Linchuan Chen, Gagan Agrawal. Revealing Parallel Scans and Reductions in Recurrences through Function Reconstruction. The 27th International Conference on Parallel Architectures and Compilation Techniques 
• Peng Jiang, Gagan Agrawal. A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. Thirty-second Conference on Neural Information Processing Systems 
• Peng Jiang, Gagan Agrawal. Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation. Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• Peng Jiang, Gagan Agrawal. Efficient SIMD and MIMD Parallelization of Hash-based Aggregation by Conflict Mitigation. Proceedings of the 2017 International Conference on Supercomputing