1 citations · 1 across the 4 of their papers we have counts for
4 papers
AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
Zepeng Li, Jie Ren, Zhanyong Tang +2
Large Language Models (LLMs) show promise for code compilation tasks, but applying them to runtime performance tuning is difficult due to complex microarchitectural effects and noi…
Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation
Yuntian Chen, Zhanyong Tang, Tianpei Lu +3
Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensit…
Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization
Huanting Wang, Patrick Lenihan, Zheng Wang
Supervised machine learning techniques have shown promising results in code analysis and optimization problems. However, a learning-based solution can be brittle because minor chan…
Moving beyond Deletions: Program Simplification via Diverse Program Transformations
Haibo Wang, Zezhong Xing, Zheng Wang +2
To reduce the complexity of software, Developers manually simplify program (known as developer-induced program simplification in this paper) to reduce its code size yet preserving…