Scaling Distributed Machine Learning with In-Network Aggregation

Code

• github.com/hesingh/p4-ipv6-switch-ml
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• github.com/IETF-Hackathon/p4-ipv6-switch-ml
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Abstract

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 5.5 for a number of real-world benchmark models.

Tasks

Reproductions