Scaling Distributed Machine Learning with In-Network Aggregation

Abstract

Training complex 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 a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.