Increasing the mini-batch size for stochastic gradient descent offers significant opportunities to reduce wall-clock training time, but there are a variety of theoretical and systems challenges that impede the widespread success of this technique (Das et al., 2016;Keskar et al., 2016). We investigate these issues, with an emphasis on time to convergence and total computational cost, through an extensive empirical analysis of network training across several architectures and problem domains, including image classification, image segmentation, and language modeling. Although it is common practice to increase the batch size in order to fully exploit available computational resources, we find a substantially more nuanced picture. Our main finding is that across a wide range of network architectures and problem domains, increasing the batch size beyond a certain point yields no decrease in wall-clock time to convergence for either train or test loss. This batch size is usually substantially below the capacity of current systems. We show that popular training strategies for large batch size optimization begin to fail before we can populate all available compute resources, and we show that the point at which these methods break down depends more on attributes like model architecture and data complexity than it does directly on the size of the dataset.
The work presented relates to the impact batch-size on the learning performances of common neural network architectures.
Pro: having comprehensive study of the limit of gradient-based methods is very useful in practice. This work can help practitioner to limit the number of machines used for optimization.
Cons: very little can be deduced from these experiments:
- "Increasing the batch size beyond a certain point yields no improvement in wall-clock time to convergence, even for a system with perfect parallelism." was a know fact (they cite Ma et al (2017) who even proved it theoretically.
- "Increasing the batch size leads to a significant increase in generalization error, which cannot be mitigated by existing techniques.". It is not clear that all the regularization techniques have been tried by the authors, the increase of generalization error is very small, and there is no explanation or insight given by the authors to explain this phenomenon, making this finding of limited interest.
- "Dataset size is not the only factor determining the computational efficiency of large batch training." is something obvious to say, as there are plenty of factors that determine the computational efficiency (network connection, map-reduce implementation, etc.)
Even the suggestions for future work of the authors in the conclusion does not help much: they suggest to look at "alternative forms of parallelism", without citing or giving any clue of what could be such alternative forms. 
Also, there is no discussion around lock-free
The authors refer to Ma et al. (2017) for a theoretical analysis of the effect of the batch size, but they skip all the past and very relevant literature on the topic of the effect of the batch size on the convergence. For example, it is recommended to increase the size of the batch size as the iterations increase.
Finally, there is no discussion on the lock-free gradient descent, that is often suggested as an alternative to batching.
In conclusion, I'm not convinced there is enough material to accept this paper at the next ICLR conference.