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arXiv · 1903.06701

Scaling Distributed Machine Learning with In-Network Aggregation

Amedeo Sapio·Marco Canini·Chen-Yu Ho·Jacob Nelson·Panos Kalnis·Changhoon Kim·Arvind Krishnamurthy·Masoud Moshref·Dan R. K. Ports·Peter Richtárik
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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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Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik. 2019-02-22. Scaling Distributed Machine Learning with In-Network Aggregation. https://arxiv.org/abs/1903.06701

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