1. Nvidia Deep Learning Performance Guide,https://docs.nvidia.com/deeplearning/sdk/dl-performance-guide/index.html

2. Pytorch,https://pytorch.org/

3. Amaral, M., Polo, J., Carrera, D., Seelam, S., Steinder, M.: Topology-aware GPU scheduling for learning workloads in cloud environments. In: ACM SC (2017)

   Google Scholar 

4. Bhuiyan, A., Guo, Z., Saifullah, A., Guan, N., Xiong, H.: Energy-efficient real-time scheduling of DAG tasks. ACM TECS17, 1–25 (2018)

   Article  Google Scholar 

5. Chaudhary, S., et al.: Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning. In: ACM EuroSys 2020 (2020)

   Google Scholar 

6. Chen, Q., Yang, H., et al.: Prophet: precise QoS prediction on non-preemptive accelerators to improve utilization in warehouse-scale computers. In: ACM SIGOPS Operating Systems Review (2017)

   Google Scholar 

7. Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Advances in Neural Information Processing Systems, pp. 4467–4475 (2017)

   Google Scholar 

8. Delimitrou, C., Kozyrakis, C.: Paragon: QoS-aware scheduling for heterogeneous datacenters. In: ACM SIGPLAN Notices. ACM (2013)

   Google Scholar 

9. Delimitrou, C., Kozyrakis, C.: Quasar: resource-efficient and QoS-aware cluster management. In: ACM ASPLOS (2014)

   Google Scholar 

10. Gardner, M., Grus, J., Neumann, M., Tafjord, O., et al.: AllenNLP: a deep semantic natural language processing platform (2017)

    Google Scholar 

11. Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)

    Google Scholar 

12. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

13. Gu, J., Chowdhury, M., Shin, K.G., Zhu, Y., et al.: Tiresias: a\(\{\)GPU\(\}\) cluster manager for distributed deep learning. In: USENIX NSDI (2019)

    Google Scholar 

14. Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks. In: IEEE CVPR, pp. 5927–5935 (2017)

    Google Scholar 

15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE CVPR (2016)

    Google Scholar 

16. Hightower, K., Burns, B., Beda, J.: Kubernetes: Up and Running: Dive into the Future of Infrastructure. O’Reilly Media Inc., Sebastopol (2017)

    Google Scholar 

17. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE CVPR, pp. 4700–4708 (2017)

    Google Scholar 

18. Iandola, F.N., Han, S., et al.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and\(<\)0.5 mb model size. arXiv preprintarXiv:1602.07360 (2016)

19. Jeon, M., et al.: Analysis of large-scale multi-tenant GPU clusters for DNN training workloads. arXiv preprintarXiv:1901.05758 (2019)

20. Kambatla, K., Yarlagadda, V., Goiri, Í., Grama, A.: UBIS: utilization-aware cluster scheduling. In: IEEE IPDPS (2018)

    Google Scholar 

21. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep, Citeseer (2009)

    Google Scholar 

22. Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: ECCV (2018)

    Google Scholar 

23. Mars, J., Tang, L., et al.: Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: IEEE/ACM MICRO (2011)

    Google Scholar 

24. Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. arXiv preprintarXiv:1609.07843 (2016)

25. Peng, Y., Bao, Y., Chen, Y., Wu, C., Guo, C.: Optimus: an efficient dynamic resource scheduler for deep learning clusters. In: ACM EuroSys (2018)

    Google Scholar 

26. Phull, R., et al.: Interference-driven resource management for GPU-based heterogeneous clusters. In: Proceedings of HDPC. ACM (2012)

    Google Scholar 

27. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: IEEE CVPR, pp. 4510–4520 (2018)

    Google Scholar 

28. Schwarzkopf, M., Konwinski, A., Abd-El-Malek, M., Wilkes, J.: Omega: flexible, scalable schedulers for large compute clusters. In: ACM EuroSys (2013)

    Google Scholar 

29. Shen, H., et al.: Nexus: a GPU cluster engine for accelerating DNN-based video analysis. In: ACM SOSP (2019)

    Google Scholar 

30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRRarXiv:1409.1556 (2014)

31. Szegedy, C., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

32. Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprintarXiv:1905.11946 (2019)

33. Tan, M., et al.: MNASNet: platform-aware neural architecture search for mobile. In: IEEE CVPR, pp. 2820–2828 (2019)

    Google Scholar 

34. Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: NIPS (2017)

    Google Scholar 

35. Vavilapalli, V.K., et al.: Apache hadoop yarn: yet another resource negotiator. In: ACM SoCC (2013)

    Google Scholar 

36. (WMT19), A.M.T.: Shared task: machine translation of news.http://www.statmt.org/wmt19/translation-task.html

37. Xiao, W., et al.: Gandiva: introspective cluster scheduling for deep learning. In: USENIX OSDI (2018)

    Google Scholar 

38. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: IEEE CVPR (2017)

    Google Scholar 

39. Xu, X., et al.: Characterization and prediction of performance interference on mediated passthrough GPUs for interference-aware scheduler. In: HotCloud (2019)

    Google Scholar 

40. Yeung, G.F., Borowiec, D., Friday, A., Harper, R., Garraghan, P.: Towards GPU utilization prediction for cloud deep learning. In: USENIX HotCloud (2020)

    Google Scholar 

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