Accelerating Towards Larger Deep Learning Models and Datasets – A System Platform View Point

Abstract

Deep Learning (DL) is a rapidly evolving field under the umbrella of Artificial Intelligence (AI) with proven real-world use cases in supervised and unsupervised learning tasks. As the complexity of the learning tasks increases, the DL models become deeper or wider with millions of parameters and use larger datasets. Neural networks like AmoebaNet with 557M parameters and GPT-2 with 1.5 billion parameters are some of the recent examples of large models. DL trainings are generally run on accelerated hardware such as GPUs, TPUs or FPGAs which can satisfy the high computational demands of the neural network training. But accelerators are limited in their memory capacities. Larger the models, larger the memory required while training them. Hence, large DL models and large datasets cannot fit into the limited memory available on GPUs. However, there are techniques designed to overcome this limitation like compression, using CPU memory as a data swap, recomputations within the GPUs etc. But the efficiency of each of these techniques also depends on the underneath system platform capabilities. In this paper we present the observations from our study of training large DL models using data swap method on different system platforms. This study showcases the characteristics of large models and presents the system viewpoint of large deep learning model training by studying the relation of the software techniques to the system platform used underneath. The results presented in the paper show that for training large Deep Learning models, communication link between CPU and GPU is critical and the training performance can be improved by using a platform with high bandwidth link for this communication. The results presented are based on two DL models, 3DUnetCNN model for medical image segmentation and DeepLabV3+ model for semantic image segmentation.