Abstract-Deep learning applications are computationintensive 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.
DeepProf REF is a tool that can automatically process GPU traces and generate performance reports for deep learning applications, which can perform diagnosis to identify the runtime bottleneck.
DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns
ArXiv
ArXiv
Computer Science