Hybrid Approach to Parallel Stochastic Gradient Descent
This work addresses efficiency issues in parallel training for machine learning practitioners, but it is incremental as it builds on existing synchronous and asynchronous approaches.
The paper tackles the problem of training neural networks on large datasets by proposing a hybrid data parallelism approach that combines synchronous and asynchronous methods, showing it outperforms both in a given time period when a threshold function is appropriately selected.
Stochastic Gradient Descent is used for large datasets to train models to reduce the training time. On top of that data parallelism is widely used as a method to efficiently train neural networks using multiple worker nodes in parallel. Synchronous and asynchronous approach to data parallelism is used by most systems to train the model in parallel. However, both of them have their drawbacks. We propose a third approach to data parallelism which is a hybrid between synchronous and asynchronous approaches, using both approaches to train the neural network. When the threshold function is selected appropriately to gradually shift all parameter aggregation from asynchronous to synchronous, we show that in a given time period our hybrid approach outperforms both asynchronous and synchronous approaches.