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.
|Original language||English (US)|
|Title of host publication||18th USENIX Symposium on Networked Systems Design and Implementation|
|Number of pages||24|
|State||Published - 2021|