Dynamic Memory Management for GPU-Based Training of Deep Neural Networks

Deep learning has been widely adopted for different applications of artificial intelligence - speech recognition, natural language processing, computer vision etc. The growing size of Deep Neural Networks (DNNs) has compelled the researchers to design memory efficient and performance optimal algorithms. Apart from algorithmic improvements, specialized hardware like Graphics Processing Units (GPUs) are being widely employed to accelerate the training and inference phases of deep networks. However, the limited GPU memory capacity limits the upper bound on the size of networks that can be offloaded to and trained using GPUs. vDNN addresses the GPU memory bottleneck issue and provides a solution which enables training of deep networks that are larger than GPU memory. In our work, we characterize and identify multiple bottlenecks with vDNN like delayed computation start, high pinned memory requirements and GPU memory fragmentation. We present vDNN++ which extends vDNN and resolves the identified issues. Our results show that the performance of vDNN++ is comparable or better (up to 60% relative improvement) than vDNN. We propose different heuristics and order for memory allocation, and empirically evaluate the extent of memory fragmentation with them. We are also able to reduce the pinned memory requirement by up to 60%.