Performance benchmarking of deep learning framework on Intel Xeon Phi

Abstract

With the success of deep learning (DL) methods in diverse application domains, several deep learning software frameworks have been proposed to facilitate the usage of these methods. By knowing the frameworks which are employed in big data analysis, the analysis process will be more efficient in terms of time and accuracy. Thus, benchmarking DL software frameworks is in high demand. This paper presents a comparative study of deep learning frameworks, namely Caffe and TensorFlow on performance metrics: runtime performance and accuracy. This study is performed with several datasets, such as LeNet MNIST classification model, CIFAR-10 image recognition datasets and message passing interface (MPI) parallel matrix-vector multiplication. We evaluate the performance of the above frameworks when employed on machines of Intel Xeon Phi 7210. In this study, the use of vectorization, OpenMP parallel processing, and MPI are examined to improve the performance of deep learning frameworks. The experimental results show the accuracy comparison between the number of iterations of the test in the training model and the training time on the different machines before and after optimization. In addition, an experiment on two multi-nodes of Xeon Phi is performed. The experimental results also show the optimization of Xeon Phi is beneficial to the Caffe and TensorFlow frameworks.

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References

  1. 1.

    Ben-Nun T, Besta M, Huber S, Ziogas AN, Peter D, Hoefler T (2019) A modular benchmarking infrastructure for high-performance and reproducible deep learning. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, pp 66–77

  2. 2.

    Blackford LS, Petitet A, Pozo R, Remington K, Whaley RC, Demmel J, Dongarra J, Duff I, Hammarling S, Henry G et al (2002) An updated set of basic linear algebra subprograms (blas). ACM Trans Math Softw 28(2):135–151

    MathSciNet  Article  Google Scholar 

  3. 3.

    Bottleson J, Kim S, Andrews J, Bindu P, Murthy DN, Jin J (2016) Clcaffe: Opencl accelerated caffe for convolutional neural networks. In: Proceedings—2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016, pp 50–57. www.scopus.com

  4. 4.

    Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Jackel LD, LeCun Y, Muller UA, Sackinger E, Simard P et al (1994) Comparison of classifier methods: a case study in handwritten digit recognition. In: Pattern Recognition, 1994. Vol 2-Conference B: Computer Vision & Image Processing. Proceedings of the 12th IAPR International. Conference on, vol 2. IEEE, pp. 77–82

  5. 5.

    Cifar10 (2017). https://www.cs.toronto.edu/~kriz/cifar.html

  6. 6.

    Coates A, Ng A, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp 215–223

  7. 7.

    Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for lvcsr using rectified linear units and dropout. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 8609–8613

  8. 8.

    Deng L, Liu Y (2018) Deep learning in natural language processing. Springer, Berlin

    Google Scholar 

  9. 9.

    Docker (2019). https://www.docker.com/

  10. 10.

    Gold S, Rangarajan A et al (1996) Softmax to softassign: neural network algorithms for combinatorial optimization. J Artif Neural Netw 2(4):381–399

    Google Scholar 

  11. 11.

    Gropp W, Lusk E, Doss N, Skjellum A (1996) A high-performance, portable implementation of the mpi message passing interface standard. Parallel Comput 22(6):789–828

    Article  Google Scholar 

  12. 12.

    Gropp W, Lusk E, Skjellum A (1999) Using MPI: portable parallel programming with the message-passing interface, vol 1. MIT Press, Cambridge

    Google Scholar 

  13. 13.

    Gropp W, Lusk E, Thakur R (1999) Using MPI-2: advanced features of the message-passing interface. MIT Press, Cambridge

    Google Scholar 

  14. 14.

    Grupp A, Kozlov V, Campos I, David M, Gomes J, García Á L (2019) Benchmarking deep learning infrastructures by means of tensorflow and containers. In: International Conference on High Performance Computing. Springer, pp 478–489

  15. 15.

    Hacker SK (2018) Mastering docker: a quick-start beginner’s guide. CreateSpace Independent Publishing Platform. https://dl.acm.org/doi/book/10.5555/3235203

  16. 16.

    Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100

    Article  Google Scholar 

  17. 17.

    Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp 1135–1143

  18. 18.

    Hegde G, Ramasamy N, Kapre N et al (2016) Caffepresso: an optimized library for deep learning on embedded accelerator-based platforms. In: 2016 International Conference on Compliers, Architectures, and Sythesis of Embedded Systems (CASES). IEEE, pp 1–10

  19. 19.

    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: MM 2014—Proceedings of the 2014 ACM Conference on Multimedia, pp 675–678. www.scopus.com

  20. 20.

    Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882

  21. 21.

    Kristiani E, Yang CT, Wang YT, Huang CY, Ko PC (2018) Container-based virtualization for real-time data streaming processing on the edge computing architecture. In: International Wireless Internet Conference. Springer, pp 203–211

  22. 22.

    Krizhevsky A, Hinton G (2010) Convolutional deep belief networks on cifar-10. Unpublished manuscript 40

  23. 23.

    Kurth T, Smorkalov M, Mendygral P, Sridharan S, Mathuriya A (2018) Tensorflow at scale: performance and productivity analysis of distributed training with horovod, mlsl, and cray pe ml. Concurrency and Computation: Practice and Experience, p e4989

  24. 24.

    Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  25. 25.

    Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318

    Article  Google Scholar 

  26. 26.

    Liu L, Wu Y, Wei W, Cao W, Sahin S, Zhang Q (2018) Benchmarking deep learning frameworks: design considerations, metrics and beyond. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp 1258–1269

  27. 27.

    Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang YC, Kim DI (2019) Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tutor 21(4):3133–3174

    Article  Google Scholar 

  28. 28.

    Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp 807–814

  29. 29.

    Nassif AB, Shahin I, Attili I, Azzeh M, Shaalan K (2019) Speech recognition using deep neural networks: a systematic review. IEEE Access 7:19143–19165

    Article  Google Scholar 

  30. 30.

    Nath R, Tomov S, Dongarra J (2010) Accelerating GPU kernels for dense linear algebra. In: VECPAR. Springer, pp 83–92

  31. 31.

    Openmpi (2017). https://www.open-mpi.org/

  32. 32.

    Panda DK, Awan AA, Subramoni H (2019) High performance distributed deep learning: a beginner’s guide. In: PPoPP, pp 452–454

  33. 33.

    Purushotham S, Meng C, Che Z, Liu Y (2018) Benchmarking deep learning models on large healthcare datasets. J Biomed Inform 83:112–134

    Article  Google Scholar 

  34. 34.

    Rosales C (2014) Porting to the intel xeon phi: opportunities and challenges. In: Proceedings—2013 Extreme Scaling Workshop, XSW 2013, pp 1–7. www.scopus.com

  35. 35.

    Roska T, Hamori J, Labos E, Lotz K, Orzó L, Takacs J, Venetianer PL, Vidnyanszky Z, Zarándy Á (1993) The use of cnn models in the subcortical visual pathway. IEEE Trans Circuits Syst I Fundam Theory Appl 40(3):182–195

    Article  Google Scholar 

  36. 36.

    Soheil B, Naveen R, Lukas S, et al (2016) Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435

  37. 37.

    Tanno R, Yanai K (2016) Caffe2c: a framework for easy implementation of cnn-based mobile applications. In: ACM International Conference Proceeding Series, vol 28-November-2016, pp 159–164. www.scopus.com

  38. 38.

    Tarasov V, Rupprecht L, Skourtis D, Li W, Rangaswami R, Zhao M (2019) Evaluating docker storage performance: from workloads to graph drivers. Cluster Computing pp 1–14

  39. 39.

    Tensorflow description (2019). https://www.tensorflow.org/

  40. 40.

    Tokic M, Palm G (2011) Value-difference based exploration: adaptive control between epsilon-greedy and softmax. KI 2011: Advances in Artificial Intelligence, pp 335–346

  41. 41.

    Venkateswaran S, Sarkar S (2019) Fitness-aware containerization service leveraging machine learning. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2019.2898666

  42. 42.

    Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci. https://doi.org/10.1155/2018/7068349

  43. 43.

    Wang H, Zhang L, Han J, Weinan E (2018) Deepmd-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun 228:178–184

    Article  Google Scholar 

  44. 44.

    Xbyak (2017). https://github.com/herumi/xbyak

  45. 45.

    Yang CT, Liu JC, Chan YW, Kristiani E, Kuo CF (2018) On construction of a caffe deep learning framework based on intel xeon phi. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Springer, pp 96–106

  46. 46.

    Yang CT, Huang CL, Lin CF (2011) Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters. Comput Phys Commun 182(1):266–269

    Article  Google Scholar 

  47. 47.

    Zarándy Á, Orzó L, Grawes E, Werblin F (1999) CNN-based models for color vision and visual illusions. IEEE Trans Circuits Syst I Fundam Theory Appl 46(2):229–238

    Article  Google Scholar 

  48. 48.

    Zhang Z, Geiger J, Pohjalainen J, Mousa AED, Jin W, Schuller B (2018) Deep learning for environmentally robust speech recognition: an overview of recent developments. ACM TIST 9(5):1–28

    Article  Google Scholar 

  49. 49.

    Zhao ZQ, Zheng P, Xu St, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Ministry of Science and Technology, Taiwan (R.O.C.), under Grant Number 108-2221-E-029-010-.

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Correspondence to Chao-Tung Yang.

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Yang, CT., Liu, JC., Chan, YW. et al. Performance benchmarking of deep learning framework on Intel Xeon Phi. J Supercomput 77, 2486–2510 (2021). https://doi.org/10.1007/s11227-020-03362-3

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Keywords

  • Intel Xeon Phi
  • Tensorflow
  • Caffe
  • Deep learning
  • Docker