Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study (1509.04210v3)

Abstract: This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learning rate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.

• The paper systematically studies the tradeoffs between model accuracy and runtime in distributed deep learning, proposing strategies like gradient staleness control, learning rate modulation, and novel synchronization protocols using the Rudra framework.
• Empirical results show that maintaining a constant product of mini-batch size and the number of learners is crucial for optimal accuracy and runtime in distributed systems.
• The findings provide practical guidance for optimizing distributed training setups and open avenues for theoretical research into scalable deep learning systems.

Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study

The paper "Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study" presents a comprehensive examination of tradeoffs in distributed deep learning systems, focusing particularly on model accuracy and runtime performance. This paper introduces "Rudra", a parameter server-based framework specifically designed to address the challenges associated with distributed training of large-scale deep neural networks using variants of asynchronous stochastic gradient descent (SGD).

Deep learning relies on the ability to efficiently train models with a vast number of parameters using large datasets, especially in tasks like image classification where models have achieved human-level precision. However, distributed training across multiple nodes is necessary to handle the increasing model sizes and datasets. This paper focuses on how various hyperparameters, such as synchronization protocols, stale gradient updates, mini-batch sizes, and learning rates, impact both model accuracy and runtime efficiency.

Through the empirical investigation using the Rudra framework, the authors proposed several strategies that effectively balance the tradeoffs between accuracy and runtime:

1. Gradient Staleness: The paper revealed the importance of controlling gradient staleness due to asynchronous updates. The vector clock mechanism offered quantifiable measures of staleness, enabling better synchronization protocols to limit its impact.
2. Learning Rate Modulation: To minimize the adverse effects of stale gradients, a new modulation strategy of learning rates was introduced. By adjusting learning rates in proportion to gradient staleness, the authors demonstrated improved convergence and model accuracy.
3. Synchronization Protocols: A novel synchronization protocol was devised to manage network bandwidth and balance runtime performance with model accuracy. The n-softsync protocol, in particular, showed promise in effectively bounding staleness while optimizing resource usage.
4. Mini-batch Size Adjustments: It was crucial to adjust the mini-batch size per learner based on the number of learners to maintain model accuracy, suggesting inherent limits to parallelism.

Empirical validations were conducted using the CIFAR10 and ImageNet benchmarks. The results underscore that reducing the mini-batch size per learner is essential as more learners are incorporated into the distributed system, maintaining the product of mini-batch size and number of learners (i.e., $\mu\lambda$) as constant proved effective for achieving optimal results.

The implications of this paper are considerable for both practical implementations and theoretical advancements in the field of artificial intelligence. Firstly, practitioners can leverage these findings to optimize distributed training setups, achieving faster training without sacrificing accuracy. Theoretically, the work provokes deeper exploration into optimizing learning strategies for systems with large mini-batch requirements and exploring algorithms suited for such conditions.

Looking ahead, this research paves the way for further exploration into tackling gradient staleness, enhancing learning rate strategies, and potentially revolutionizing distributed deep learning frameworks. It establishes a foundation for future developments and innovations in scalable machine learning systems, ensuring they remain efficient, robust, and accurate in increasingly complex data environments.