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Deep learning applications are computation-intensive and often employ GPU asthe underlying computing devices. Deep learning frameworks provide powerfulprogramming interfaces, but the gap between source codes and practical GPUoperations make it difficult to analyze the performance of deep learningapplications. In this paper, through examing the features of GPU traces anddeep learning applications, we use the suffix tree structure to extract therepeated patten in GPU traces. Performance analysis graphs can be generatedfrom the preprocessed GPU traces. We further present \texttt{DeepProf}, a noveltool to automatically process GPU traces and generate performance analysisreports for deep learning applications. Empirical study verifies theeffectiveness of \texttt{DeepProf} in performance analysis and diagnosis. Wealso find out some interesting properties of Tensorflow, which can be used toguide the deep learning system setup.

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns