Scaling Distributed Machine Learning With In-Network Aggregation
PROCEEDINGS OF THE 18TH USENIX SYMPOSIUM ON NETWORKED SYSTEM DESIGN AND IMPLEMENTATION(2021)
摘要
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 x for a number of real-world benchmark models.
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