DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns
School of Computer Science, Fudan University, Shanghai, China
arXiv:1707.03750 [cs.SE], (12 Jul 2017)
@article{gu2017deepprof,
title={DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns},
author={Gu, Jiazhen and Liu, Huan and Zhou, Yangfan and Wang, Xin},
year={2017},
month={jul},
archivePrefix={"arXiv"},
primaryClass={cs.SE}
}
Deep learning applications are computation-intensive and often employ GPU as the underlying computing devices. Deep learning frameworks provide powerful programming interfaces, but the gap between source codes and practical GPU operations make it difficult to analyze the performance of deep learning applications. In this paper, through examing the features of GPU traces and deep learning applications, we use the suffix tree structure to extract the repeated patten in GPU traces. Performance analysis graphs can be generated from the preprocessed GPU traces. We further present DeepProf, a novel tool to automatically process GPU traces and generate performance analysis reports for deep learning applications. Empirical study verifies the effectiveness of DeepProf in performance analysis and diagnosis. We also find out some interesting properties of Tensorflow, which can be used to guide the deep learning system setup.
July 14, 2017 by hgpu
