SyCCL: Exploiting Symmetry for Efficient Collective Communication Scheduling
Jiamin Cao, Shangfeng Shi, Jiaqi Gao, Weisen Liu, Yifan Yang, Yichi Xu, Zhilong Zheng, Yu Guan, Kun Qian, Ying Liu, Mingwei Xu, T. Wang, Ning Wang, Jianbo Dong, Binzhang Fu, Dennis Cai, Ennan Zhai · 2025
The performance of collective communication schedules is crucial for the efficiency of machine learning jobs and GPU cluster utilization. Existing open-source collective communication libraries (such as NCCL and RCCL) rely on fixed schedules and cannot adjust to varying topology and model requirements. State-of-the-art collective schedule synthesizers (such as TECCL and TACCL) utilize Mixed Integer Linear Program for modeling but encounter search space explosion and scalability challenges. In this paper, we propose SyCCL, a scalable collective schedule synthesizer that aims to synthesize near-optimal schedules in tens of minutes for production-scale machine-learning jobs. SyCCL leverages collective and topology symmetries to decompose the original collective communication demand into smaller sub-demands within smaller topology subsets. SyCCL proposes efficient search strategies to quickly explore potential sub-demands, synthesizes corresponding sub-schedules, and integrates these sub-schedules into complete schedules. Our 32-A100 testbed and production-scale simulation experiments show that SyCCL improves collective performance by up to 127% while reducing synthesis time by 2 to 4 orders of magnitude compared to state-of-the-art efforts.