In this report, we propose Triton-distributed, an extension of existing Triton compiler, to overcome the programming challenges in distributed AI systems. Triton-distributed is the first compiler that supports native overlapping optimizations for distributed AI workloads, providing a good coverage of existing optimizations from different frameworks. First, we integrate communication primitives compliant with the OpenSHMEM standard into the compiler. This enables programmers to utilize these primitives with a higher-level Python programming model. Second, we illustrate how to achieve complex joint optimization of computation, memory access, and communication with the assistance of the compiler. In particular, we show how to use overlapping techniques to hide latency and present our compiler-based programming methods in both single-node and multi-node scenarios. Finally, we showcase the performance of the code generated by our compiler. In a test environment with up to 64 devices, our compiler can fully utilize heterogeneous communication and computation resources to provide effective overlapping and high performance. In many cases, the performance of the generated code can even outperform hand-optimized code. Moreover, the development difficulty and the time cost for development using our compiler are far less than those of low-level programming such as CUDA/C++, which clearly demonstrates significant productivity advantages.
@article{arxiv.2504.19442,
title = {Triton-distributed: Programming Overlapping Kernels on Distributed AI Systems with the Triton Compiler},
author = {Size Zheng and Wenlei Bao and Qi Hou and Xuegui Zheng and Jin Fang and Chenhui Huang and Tianqi Li and Haojie Duanmu and Renze Chen and Ruifan Xu and Yifan Guo and Ningxin Zheng and Ziheng Jiang and Xinyi Di and Dongyang Wang and Jianxi Ye and Haibin Lin and Li-Wen Chang and Liqiang Lu and Yun Liang and Jidong Zhai and Xin Liu},
journal= {arXiv preprint arXiv:2504.19442},
year = {2025}
}