BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training
This work addresses the problem of efficient distributed training for large deep neural networks, which is significant for researchers and practitioners dealing with memory and computation constraints.
This paper introduces BaPipe, a new pipeline parallelism training framework that automatically explores balanced partition strategies for distributed deep neural network training. BaPipe achieves up to 3.2x speedup and 4x memory reduction compared to state-of-the-art data and pipeline parallelism frameworks.
The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN training, distributed deep learning based on model parallelism has been widely recognized. We propose a new pipeline parallelism training framework, BaPipe, which can automatically explore pipeline parallelism training methods and balanced partition strategies for DNN distributed training. In BaPipe, each accelerator calculates the forward propagation and backward propagation of different parts of networks to implement the intra-batch pipeline parallelism strategy. BaPipe uses a new load balancing automatic exploration strategy that considers the parameters of DNN models and the computation, memory, and communication resources of accelerator clusters. We have trained different DNNs such as VGG-16, ResNet-50, and GNMT on GPU clusters and simulated the performance of different FPGA clusters. Compared with state-of-the-art data parallelism and pipeline parallelism frameworks, BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.