We show communication schedulers’ recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also often produce suboptimal schedules. We make a connection with similar problems in traffic engineering and propose a new method, TE-CCL, that finds better uality schedules (e.g., finishes collectives faster and/or while sending fewer bytes) and does so more quickly on larger topologies. We present results on many different GPU topologies that show substantial improvement over the state-of-the-art.

BibTeX Citation

      title={Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem}, 
      author={Behnaz Arzani and Siva Kesava Reddy Kakarla and Miguel Castro and Srikanth Kandula and Saeed Maleki and Luke Marshall},