The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.

MLA

Oyama, Yosuke, et al. “The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.” IEEE Transactions on Parallel & Distributed Systems, vol. 32, no. 7, July 2021, pp. 1641–52. EBSCOhost, https://doi.org/10.1109/TPDS.2020.3047974.

APA

Oyama, Y., Maruyama, N., Dryden, N., McCarthy, E., Harrington, P., Balewski, J., Matsuoka, S., Nugent, P., & Van Essen, B. (2021). The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism. IEEE Transactions on Parallel & Distributed Systems, 32(7), 1641–1652. https://doi.org/10.1109/TPDS.2020.3047974

Chicago

Oyama, Yosuke, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, and Brian Van Essen. 2021. “The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism.” IEEE Transactions on Parallel & Distributed Systems 32 (7): 1641–52. doi:10.1109/TPDS.2020.3047974.