Abstract
The capacity of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and storage of DNN training, distributed training methods based on model parallelism have 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, storage, and communication resources of accelerator clusters. We have trained different DNNs such as VGG-16, ResNet-50, and GNMT in GPU clusters and simulated the performance with 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.
Abstract (translated)
URL
https://arxiv.org/abs/2012.12544
