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• W2967893498 abstract "State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance profiling efforts and often relies on domain-specific knowledge. This paper takes a deep dive into analyzing the performance impact of key design features in a machine learning framework and quantifies the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. Across a diverse set of real-world deep learning models, the evaluation results show that the proposed performance tuning guidelines outperform the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively." @default.
• W2967893498 created "2019-08-22" @default.
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• W2967893498 date "2019-08-13" @default.
• W2967893498 modified "2023-10-01" @default.
• W2967893498 title "Exploiting Parallelism Opportunities with Deep Learning Frameworks" @default.
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• W2967893498 hasPublicationYear "2019" @default.
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