Efficient Multi-GPU Memory Management for Deep Learning Acceleration

In this paper, we propose a new optimized memory management scheme that can improve the overall GPU memory utilization in multi-GPU systems for deep learning application acceleration. We extend the Nvidia's vDNN concept (a hybrid utilization of GPU and CPU memories) in a multi-GPU environment by effectively addressing PCIe-bus contention problems. In addition, we designed and implemented an intelligent prefetching algorithm (from CPU memory to GPU) that can achieve the highest processing throughput while sustaining a large min-batch size. For evaluation, we have implemented our memory usage optimization scheme on Tensorflow, the well-known machine learning library from Google, and performed extensive experiments in a multi-GPU testbed. Our evaluation results show that the proposed scheme can increase the mini-batch size by up to 60%, and improve the training throughput by up to 46.6% in a multi-GPU system.