Rethinking Machine Learning Collective Communication as a Multi Commodity Flow problem - Liu, Xuting; Arzani, Behnaz; Kakarla, Siva Kesava Reddy; Zhao, Liangyu; Liu, Vincent; Castro, Miguel; Kandula, Srikanth; Marshall, Luke (SIGCOMM, 2024)
Cloud operators utilize collective communication optimizers to enhance the efficiency of the single-tenant, centrally managed training clusters they manage. However, current optimizers struggle to scale for such use cases and often compromise solution quality for scalability. Our solution, TE-CCL, adopts a traffic-engineering-based approach to collective communication. Compared to a state-of-the-art optimizer, TACCL, TE-CCL produced schedules with 2× better performance on topologies TACCL supports (and its solver took a similar amount of time as TACCL's heuristic-based approach). TECCL additionally scales to larger topologies than TACCL. On our GPU testbed, TE-CCL outperformed TACCL by 2.14× and RCCL by 3.18× in terms of algorithm bandwidth.