Integrated Model and Data Parallelism in Training Neural Networks
By A. Gholami, Ariful Azad, Kurt Keutzer, Aydin Buluç. 2017
12 citations recorded by the source corpus.
Subfields: Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Optimization & Training
- A. Gholami
- Kurt Keutzer
- HyoukJoong Lee
- Ashish Vaswani
- Blake A. Hechtman
- Noam Shazeer
- Peter G. Hawkins
- Mingsheng Hong
Citations in the corpus
- Mesh-TensorFlow: Deep Learning for Supercomputers (2018, methodology): “[19] analytically showed that using both data and model parallelism at the same time can be more beneficial than using just one of them.”
- Integrated Model, Batch, and Domain Parallelism in Training Neural Networks (2017, background): “1 The derivations of the forward pass and the backpropagation are shown in detail in the extended version of this paper [8]).”