Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning

Igor Colin, Ludovic Dos Santos, Kevin Scaman. Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Edward A. Fox, Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada. pages 12350-12359, 2019. [doi]

@inproceedings{ColinSS19,
  title = {Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning},
  author = {Igor Colin and Ludovic Dos Santos and Kevin Scaman},
  year = {2019},
  url = {http://papers.nips.cc/paper/9402-theoretical-limits-of-pipeline-parallel-optimization-and-application-to-distributed-deep-learning},
  researchr = {https://researchr.org/publication/ColinSS19},
  cites = {0},
  citedby = {0},
  pages = {12350-12359},
  booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada},
  editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alché-Buc and Edward A. Fox and Roman Garnett},
}