Optimal GPU-CPU Offloading Strategies for Deep Neural Network Training

Author:

Beaumont OlivierORCID,Eyraud-Dubois LionelORCID,Shilova AlenaORCID

Publisher

Springer International Publishing

Reference21 articles.

1. Shriram, S.B., Garg, A., Kulkarni, P.: Dynamic memory management for GPU-based training of deep neural networks. In: IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE Press (2019). https://doi.org/10.1109/IPDPS.2019.00030

2. Beaumont, O., Eyraud-Dubois, L., Herrmann, J., Joly, A., Shilova, A.: Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory. Research Report RR-9302, Inria Bordeaux Sud-Ouest, November 2019. https://hal.inria.fr/hal-02352969

3. Beaumont, O., Eyraud-Dubois, L., Shilova, A.: Optimal GPU-CPU Offloading Strategies for Deep Neural Network Training, October 2019. https://hal.inria.fr/hal-02316266, working paper or preprint

4. Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., Joly, A.: Going deeper in the automated identification of herbarium specimens. BMC Evol. Biol. 17(1), 181 (2017). https://doi.org/10.1186/s12862-017-1014-z

5. Chen, T., Xu, B., Zhang, C., Guestrin, C.: Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174 (2016)

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