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Scaling Distributed Machine Learning with In-Network Aggregation
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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$\times$ for a number of real-world benchmark models.
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Cited by 1 Pith paper
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In-Network Market Prediction Using Machine Learning and Limit Order Books
A programmable switch can maintain a limit order book and run ML price-movement inference in the data plane, achieving microsecond latency with accuracy close to server benchmarks.
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