{"id":735949,"date":"2021-03-24T18:41:05","date_gmt":"2021-03-25T01:41:05","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=735949"},"modified":"2021-03-24T18:42:12","modified_gmt":"2021-03-25T01:42:12","slug":"scaling-distributed-machine-learning-with-in-network-aggregation-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/scaling-distributed-machine-learning-with-in-network-aggregation-2\/","title":{"rendered":"Scaling Distributed Machine Learning with In-Network Aggregation"},"content":{"rendered":"<div class=\"row\">\n<div class=\"col datacolumn\">\n<div class=\"pub-details\">\n<div class=\"abstract well\" data-ex-content=\".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 5.5x for a number of real-world benchmark models.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"container-fluid publication\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>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 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