On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
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 (Daset 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.
 This paper empirically investigates the effect of batch size on the convergence speed of the mini-batch stochastic gradient descent of popular deep learning models. The fact that there is a diminishing return of batch size is not very surprising and there is a well-known theory behind it, but the theory doesn*t exactly tell when we will start to suffer from the diminishing return. Therefore, it is quite valuable for the community to have an empirical analysis across popular ML tasks and models. In this regard, however, It would*ve been even nicer if the paper covered more variety of popular ML models such as Machine Translation, Speech Recognition, (Conditional) Image Generation, etc which open source implementations are readily available. Otherwise, experiments in this paper are pretty comprehensive. The only additional experiment I would be interested in is to tune learning rate for each batch size, rather than using a base learning rate everywhere, or simple rules such as LSR or SRSR. Since the theory only gives us asymptotic form of the optimal learning rate, empirically you should be tuning the learning rate for each batch size. And this is not totally unrealistic, because you can use a fraction of computational time to do cross-validation for searching the learning rate. pros: * findings provide us useful direction for future research (that data-parallelism centered distributed training is going to hit the limit soon) * extensive experiments across 5 datasets and 6 neural network architectures cons: * experiments are a bit too much focused on image classification * error bars in figures could*ve provided greater confidence in robustness of findings
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
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 (Daset 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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper empirically investigates the effect of batch size on the convergence speed of the mini-batch stochastic gradient descent of popular deep learning models. The fact that there is a diminishing return of batch size is not very surprising and there is a well-known theory behind it, but the theory doesn*t exactly tell when we will start to suffer from the diminishing return. Therefore, it is quite valuable for the community to have an empirical analysis across popular ML tasks and models. In this regard, however, It would*ve been even nicer if the paper covered more variety of popular ML models such as Machine Translation, Speech Recognition, (Conditional) Image Generation, etc which open source implementations are readily available. Otherwise, experiments in this paper are pretty comprehensive. The only additional experiment I would be interested in is to tune learning rate for each batch size, rather than using a base learning rate everywhere, or simple rules such as LSR or SRSR. Since the theory only gives us asymptotic form of the optimal learning rate, empirically you should be tuning the learning rate for each batch size. And this is not totally unrealistic, because you can use a fraction of computational time to do cross-validation for searching the learning rate. pros: * findings provide us useful direction for future research (that data-parallelism centered distributed training is going to hit the limit soon) * extensive experiments across 5 datasets and 6 neural network architectures cons: * experiments are a bit too much focused on image classification * error bars in figures could*ve provided greater confidence in robustness of findings
assistant
 This paper empirically investigates the effect of batch size on the convergence speed of the mini-batch stochastic gradient descent of popular deep learning models. The fact that there is a diminishing return of batch size is not very surprising and there is a well-known theory behind it, but the theory doesn*t exactly tell when we will start to suffer from the diminishing return. Therefore, it is quite valuable for the community to have an empirical analysis across popular ML tasks and models. In this regard, however, It would*ve been even nicer if the paper covered more variety of popular ML models such as Machine Translation, Speech Recognition, (Conditional) Image Generation, etc which open source implementations are readily available. Otherwise, experiments in this paper are pretty comprehensive. The only additional experiment I would be interested in is to tune learning rate for each batch size, rather than using a base learning rate everywhere, or simple rules such as LSR or SRSR. Since the theory only gives us asymptotic form of the optimal learning rate, empirically you should be tuning the learning rate for each batch size. And this is not totally unrealistic, because you can use a fraction of computational time to do cross-validation for searching the learning rate. pros: * findings provide us useful direction for future research (that data-parallelism centered distributed training is going to hit the limit soon) * extensive experiments across 5 datasets and 6 neural network architectures cons: * experiments are a bit too much focused on image classification * error bars in figures could*ve provided greater confidence in robustness of findings