MLCC: A Congestion Control Technique to Accelerate ML Training

We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner. At the heart of MLCC lies a straightforward principle: DNN training flows should scale their sending rate to shift other flows' communication into their compute periods, achieving interleaving. We show that integrating this principle into today's congestion control protocols is simple (requiring less than 60 lines of code for a given protocol) and enables DNN jobs to interleave within a few training iterations, thereby reducing network contention and improving job completion times. Our testbed demonstrates that MLCC accelerates the average and 99th percentile training iteration times by up to 1.9x and 2.7x respectively. Through extensive packet-level simulations, we observe a 1.35x improvement in training throughput on a 36-node, 288 GPU fat-tree topology.