Scaling Distributed Machine Learning with In-Network Aggregation

  • Amedeo Sapio
  • Marco Canini
  • Chen-Yu Ho
  • Panos Kalnis
  • Changhoon Kim
  • Arvind Krishnamurthy
  • Masoud Moshref
  • Peter Richtarik

NSDI 2021 |

Organized by USENIX

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Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide an efficient solution that speeds up training by up to 5.5x for a number of real-world benchmark models.