Bridging the Semantic Gaps of GPU Acceleration for Scale-out CNN-based Big Data Processing
Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li · 2016
Convolutional Neural Networks (CNNs) have substantially advanced the state-of-the-art accuracies of object recognition, which is the core function of a myriad of modern multimedia processing techniques such as image/video processing, speech recognition, and natural language processing. GPU-based accelerators gained increasing attention because a large amount of highly parallel neurons in CNN naturally matches the GPU computation pattern. In this work, we perform comprehensive experiments to investigate the performance bottlenecks and overheads of current GPU acceleration platform for scale-out CNN-based big data processing.