High-performance GPU kernel optimization remains a critical yet labor-intensive task in modern machine learning workloads. Although Triton, a domain-specific language for GPU programming, enables developers to write efficient kernels with concise code, achieving expert-level performance still requires deep understanding of GPU architectures and low-level performance trade-offs. We present TritonForge, a profiling-guided framework for automated Triton kernel optimization. TritonForge integrates kernel analysis, runtime profiling, and iterative code transformation to streamline the optimization process. By incorporating feedback from profiling results, the system identifies performance bottlenecks, proposes targeted code modifications, and evaluates their impact automatically. Across diverse kernel types, TritonForge achieves up to 5x performance improvement over baseline implementations and on average 1.76x of the cases are successful, providing a foundation for future research in automated GPU performance optimization.
@article{arxiv.2512.09196,
title = {TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization},
author = {Haonan Li and Keyu Man and Partha Kanuparthy and Hanning Chen and Wei Sun and Sreen Tallam and Chenguang Zhu and Kevin Zhu and Zhiyun Qian},
journal= {arXiv preprint arXiv:2512.09196},
year = {2025}
}