OC-DNN: Exploiting Advanced Unified Memory Capabilities in CUDA 9 and Volta GPUs for Out-of-Core DNN Training

Abstract

Existing frameworks cannot train large DNNs that do not fit the GPU memory without explicit memory management schemes. In this paper, we propose OC-DNN - a novel Out-of-Core DNN training framework that exploits new Unified Memory features along with new hardware mechanisms in Pascal and Volta GPUs. OC-DNN has two major design components — 1) OC-Caffe; an enhanced version of Caffe that exploits innovative UM features like asynchronous prefetching, managed page-migration, exploitation of GPU-based page faults, and the cudaMemAdvise interface to enable efficient out-of-core training for very large DNNs, and 2) an interception library to transpar-ently leverage these cutting-edge features for other frameworks. We provide a comprehensive performance characterization of our designs. OC-Caffe provides comparable performance (to Caffe) for regular DNNs. OC-Caffe-Opt is up to 1.9X faster than OC-Caffe-Naive and up to 5X faster than optimized CPU-based training for out-of-core workloads. OC-Caffe also allows scale-up (DGX-1) and scale-out on multi-GPU clusters.