8 research outputs found
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
The most widely used machine learning frameworks require users to carefully
tune their memory usage so that the deep neural network (DNN) fits into the
DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to
study different machine learning algorithms, forcing them to either use a less
desirable network architecture or parallelize the processing across multiple
GPUs. We propose a runtime memory manager that virtualizes the memory usage of
DNNs such that both GPU and CPU memory can simultaneously be utilized for
training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory
usage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a
significant reduction in memory requirements of DNNs. Similar experiments on
VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the
memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256
(requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card
containing 12 GB of memory, with 18% performance loss compared to a
hypothetical, oracular GPU with enough memory to hold the entire DNN.Comment: Published as a conference paper at the 49th IEEE/ACM International
Symposium on Microarchitecture (MICRO-49), 201