Measuring the Effects of Data Parallelism on Neural Network Training (1811.03600v3)

Abstract: Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error. We study how this relationship varies with the training algorithm, model, and data set, and find extremely large variation between workloads. Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes. We find no evidence that larger batch sizes degrade out-of-sample performance. Finally, we discuss the implications of our results on efforts to train neural networks much faster in the future. Our experimental data is publicly available as a database of 71,638,836 loss measurements taken over the course of training for 168,160 individual models across 35 workloads.

• The paper demonstrates that increasing batch sizes initially reduce training steps proportionally until a maximum useful limit is reached.
• The study finds that scaling behavior is workload-dependent, varying significantly with model architecture, training algorithms, and datasets.
• The analysis highlights that advanced optimizers can leverage larger batches effectively through precise tuning of the effective learning rate.

Measuring the Effects of Data Parallelism on Neural Network Training

The paper "Measuring the Effects of Data Parallelism on Neural Network Training" rigorously explores how varying batch sizes influence the training efficiency of neural networks across different workloads. The authors, Shallue et al., have executed an exhaustive experimental paper across multiple models, optimizers, and datasets to provide a nuanced understanding of the impacts of scaling batch sizes within mini-batch stochastic gradient descent (SGD) frameworks.

The central focus of this research lies in deciphering the relationship between batch size and training time, quantified by the number of steps to achieve a target out-of-sample error. This investigation offers valuable insights into a universal scaling behavior observed across workloads, while also addressing discrepancies in the literature regarding large batch effects.

Key Findings

1. Characteristic Scaling Curve: It is established that increasing the batch size initially results in a proportional reduction in training steps (termed as perfect scaling). However, this trend is not indefinite—larger batch sizes hit a plateau beyond which further increase does not expedite training, marking the maximum useful batch size.
2. Workload Variability: A critical observation is that the maximum useful batch size and the extent of perfect scaling are workload-dependent. They vary significantly based on the model architecture, training algorithm, and dataset type, highlighting the nuanced optimization landscape of neural network training.
3. Optimization Dynamics: The experiments reveal that SGD with momentum and its variants can leverage larger batch sizes more effectively than plain SGD. However, discrepancies arise with pre-existing heuristics like linear or square-root scaling of the learning rate with the batch size; these do not hold universally across different contexts.
4. Role of Regularization: The paper suggests that large batch sizes do not inherently degrade out-of-sample performance, a contention oft-debated in earlier works. Instead, it is the regularization techniques, such as label smoothing, that become crucial, especially at larger batch sizes.
5. Metaparameter Tuning: The effective learning rate—the learning rate adjusted for momentum—plays a pivotal role and needs precise tuning rather than relying on heuristic adjustments.

Implications and Future Directions

The empirical data, comprising over 71 million measurements from various models and workloads, offers a database valuable for further analysis by the research community. Practically, these findings guide ML practitioners to understand and effectively leverage hardware capabilities for faster training without compromising model quality. Theoretically, it challenges the field to refine the current optimization paradigms and adapt metaparameter tuning strategies for maximal efficiency in data-parallel environments.

Looking forward, the field could benefit from exploring advanced optimization techniques that could inherently extend the useful batch size or further optimize the metaparameter heuristics to enhance scalability across varied workloads. Additionally, understanding how model architectures can be intrinsically aligned with batch scaling could open avenues for designing inherently scalable networks.

This extensive exploration by Shallue et al. forms a foundation that bridges the gap between theoretical considerations of batch size influence and practical applicability in modern neural network training, setting a benchmark for subsequent inquiries into efficient data-parallel optimization.