Deep Learning Models on CPUs: A Methodology for Efficient Training


  • Quchen Fu Dept. of Computer Science, Vanderbilt University Nashville, TN, USA
  • Ramesh Chukka Intel Corporation, Santa Clara, CA, USA
  • Keith Achorn Intel Corporation, Santa Clara, CA, USA
  • Thomas Atta-fosu Intel Corporation, Santa Clara, CA, USA
  • Deepak R. Canchi Intel Corporation, Santa Clara, CA, USA
  • Zhongwei Teng Dept. of Computer Science, Vanderbilt University Nashville, TN, USA
  • Jules White Dept. of Computer Science, Vanderbilt University Nashville, TN, USA
  • Douglas C. Schmidt Dept. of Computer Science, Vanderbilt University Nashville, TN, USA



Training methodology, deep learning on CPU, performance analysis


GPUs have been favored for training deep learning models due to their highly parallelized architecture. As a result, most studies on training optimization focus on GPUs. There is often a trade-off, however, between cost and efficiency when deciding how to choose the proper hardware for training. In particular, CPU servers can be beneficial if training on CPUs was more efficient, as they incur fewer hardware update costs and better utilize existing infrastructure.

This paper makes three contributions to research on training deep learning models using CPUs. First, it presents a method for optimizing the training of deep learning models on Intel CPUs and a toolkit called ProfileDNN, which we developed to improve performance profiling. Second, we describe a generic training optimization method that guides our workflow and explores several case studies where we identified performance issues and then optimized the Intel® Extension for PyTorch, resulting in an overall 2x training performance increase for the RetinaNet-ResNext50 model. Third, we show how to leverage the visualization capabilities of ProfileDNN, which enabled us to pinpoint bottlenecks and create a custom focal loss kernel that was two times faster than the official reference PyTorch implementation.


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Author Biographies

Quchen Fu, Dept. of Computer Science, Vanderbilt University Nashville, TN, USA

Quchen Fu Fu is a Ph.D. student at Vanderbilt University major in Computer Science, his research interest is NLP and Deep Learning. He got his Master’s degree in CMU and he was TA for multiple courses including Cloud Computing and Cybersecurity. He interned at multiple companies including Tencent, Intel, and Microsoft. He is now a research assistant in Magnum research group under Dr. Jules White.

Zhongwei Teng, Dept. of Computer Science, Vanderbilt University Nashville, TN, USA

Zhongwei Teng is pursuing a Ph.D. in Computer Science in Vanderbilt University. His research interests include speech verification, NLP and machine learning.

Jules White, Dept. of Computer Science, Vanderbilt University Nashville, TN, USA

Jules White is Associate Dean of Strategic Learning Programs in the School of Engineering and Associate Professor of Computer Science in the Dept. of Computer Science at Vanderbilt University. He is a National Science Foundation CAREER Award recipient. His research has won multiple Best Paper Awards. He has also published over 150 papers. Dr. White’s research focuses on cyber-security and mobile/cloud computing in domains ranging from healthcare to manufacturing. His research has been licensed and transitioned to industry, where it won an Innovation Award at CES 2013, attended by over 150,000 people, was a finalist for the Technical Achievement at Award at SXSW Interactive, and was a top 3 for mobile in the Accelerator Awards at SXSW 2013. He has raised over $12 million in venture backing for his startup companies. His research is conducted through the Mobile Application computinG, optimizatoN, and secUrity Methods (\href Group at Vanderbilt University, which he directs.

Douglas C. Schmidt, Dept. of Computer Science, Vanderbilt University Nashville, TN, USA

Douglas C. Schmidt is the Cornelius Vanderbilt Professor of Computer Science, Associate Chair of Computer Science, and a Senior Researcher at the Institute for Software Integrated Systems, all at Vanderbilt University. His research covers a range of software-related topics, including patterns, optimization techniques, and empirical analyses of frameworks and model-driven engineering tools that facilitate the development of mission-critical middleware for distributed real-time embedded (DRE) systems and intelligent mobile cloud computing applications. Dr. Schmidt received B.A. and M.A. degrees in Sociology from the College of William and Mary in Williamsburg, Virginia, and an M.S. and a Ph.D. in Computer Science from the University of California, Irvine (UCI) in 1984, 1986, 1990, and 1994, respectively.


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How to Cite

Fu, Q., Chukka, R., Achorn, K., Atta-fosu, T., Canchi, D. R., Teng, Z., White, J., & Schmidt, D. C. (2023). Deep Learning Models on CPUs: A Methodology for Efficient Training. Journal of Machine Learning Theory, Applications and Practice, 1(01), 83–106.




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