Abstract
In the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore, we measured results for various numbers of threads with ranges depending on a given processor(s) as well as compact and scatter affinities. Based on results we formulate conclusions with respect to optimal parameters and relative performances which can serve as hints for researchers training similar networks using modern processors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Czarnul, P.: Benchmarking parallel chess search in Stockfish on Intel Xeon and Intel Xeon Phi processors. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10862, pp. 457–464. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93713-7_40
Czarnul, P.: Benchmarking performance of a hybrid Intel Xeon/Xeon Phi system for parallel computation of similarity measures between large vectors. Int. J. Parallel Program. 45, 1091–1107 (2017). https://doi.org/10.1007/s10766-016-0455-0
Krzywaniak, A., Proficz, J., Czarnul, P.: Analyzing energy/performance trade-offs with power capping for parallel applications on modern multi and many core processors. In: FedCSIS, pp. 339–346 (2018)
Shi, S., Wang, Q., Xu, P., Chu, X.: Benchmarking state-of-the-art deep learning software tools. In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pp. 99–104 (2016)
Serpa, M.S., Krause, A.M., Cruz, E.H.M., Navaux, P.O.A., Pasin, M., Felber, P.: Optimizing machine learning algorithms on multi-core and many-core architectures using thread and data mapping. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 329–333 (2018)
Alzantot, M., Wang, Y., Ren, Z., Srivastava, M.B.: RSTensorFlow: GPU enabled TensorFlow for deep learning on commodity Android devices. In: MobiSys, pp. 7–12 (2017). https://doi.org/10.1145/3089801.3089805
Awan, A.A., Subramoni, H., Panda, D.K.: An in-depth performance characterization of CPU- and GPU-based DNN training on modern architectures. In: Proceedings of the Machine Learning on HPC Environments, MLHPC 2017, pp. 8:1–8:8. ACM, New York (2017)
Dong, S., Kaeli, D.: DNNMark: a deep neural network benchmark suite for GPUs. In: Proceedings of the General Purpose GPUs, GPGPU 2010, pp. 63–72. ACM, New York (2017)
Karki, A., Keshava, C.P., Shivakumar, S.M., Skow, J., Hegde, G.M., Jeon, H.: Tango: a deep neural network benchmark suite for various accelerators (2019)
Barney, L.: Can FPGAs beat GPUs in accelerating next-generation deep learning? (2017). The Next Platform. https://www.nextplatform.com/2017/03/21/can-fpgas-beat-gpus-accelerating-next-generation-deep-learning/
Sharma, H., et al.: From high-level deep neural models to FPGAs. In: 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 1–12 (2016)
Seppälä, S.: Performance of neural network image classification on mobile CPU and GPU. Master’s thesis, Aalto University (2018)
Ignatov, A., et al.: AI benchmark: running deep neural networks on Android smartphones. CoRR abs/1810.01109 (2018)
Vanhoucke, V., Senior, A., Mao, M.Z.: Improving the speed of neural networks on CPUs. In: Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011 (2011)
Wang, Y., Wei, G., Brooks, D.: Benchmarking TPU, GPU, and CPU platforms for deep learning. CoRR abs/1907.10701 (2019)
Czarnul, P., Proficz, J., Krzywaniak, A.: Energy-aware high-performance computing: survey of state-of-the-art tools, techniques, and environments. Sci. Program. 2019 (2019). Article ID. 8348791. https://doi.org/10.1155/2019/8348791
Acknowledgments
This work is partially supported by Intel and the Intel Labs Academic Compute Environment. Work partially performed within statutory activities of Dept. of Computer Architecture, Faculty of ETI, Gdańsk University of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jabłońska, K., Czarnul, P. (2020). Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-47679-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-47678-6
Online ISBN: 978-3-030-47679-3
eBook Packages: Computer ScienceComputer Science (R0)