  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, TECCL, that finds better quality 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.
