What is PipeDream?
PipeDream is a parallel strategy used for training large neural networks. It is an asynchronous pipeline parallel strategy that helps improve the parallel training throughput, by adding inter-batch pipelining to intra-batch parallelism. This strategy helps reduce the amount of communication needed during training, while also better overlapping computation with communication.
How does PipeDream work?
PipeDream was developed to help with the training of very large neural networks. It does this by adding inter-batch pipelining to intra-batch parallelism. Inter-batch pipelining involves breaking the training data into multiple batches and applying different parts of the neural network to each.
Intra-batch parallelism, on the other hand, involves splitting each batch into smaller parts, and applying them to different processors. This approach helps reduce the amount of time it takes to train large neural networks, as each processor can work on a small part of the data, reducing computation time.
The final result is that PipeDream is able to produce much faster training times and better model accuracy compared to other strategies.
Advantages of PipeDream
PipeDream offers several advantages over other parallel training strategies. Firstly, it offers faster training times, meaning you can train large datasets quickly and efficiently. Secondly, it offers better model accuracy, as it utilizes the full power of your parallel system.
Another advantage of PipeDream is that it's easy to implement in your existing neural network training code. It doesn't require any special hardware, and the process can be easily adapted to any network you want to train. Additionally, it reduces the amount of communication needed between processors during training, which can help reduce costs associated with training large neural networks, such as increased energy consumption.
Conclusion
PipeDream is an innovative and effective pipeline parallel strategy for training large neural networks. With its ability to reduce communication when possible and maximize computation overlap with communication, it offers faster training times and better model accuracy compared to other parallel strategies. Its ease of implementation and ability to work on existing neural network training codes make it an ideal solution for those looking to train large datasets quickly and efficiently.
