Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD (1810.08313v2)

Abstract: Large-scale machine learning training, in particular distributed stochastic gradient descent, needs to be robust to inherent system variability such as node straggling and random communication delays. This work considers a distributed training framework where each worker node is allowed to perform local model updates and the resulting models are averaged periodically. We analyze the true speed of error convergence with respect to wall-clock time (instead of the number of iterations), and analyze how it is affected by the frequency of averaging. The main contribution is the design of AdaComm, an adaptive communication strategy that starts with infrequent averaging to save communication delay and improve convergence speed, and then increases the communication frequency in order to achieve a low error floor. Rigorous experiments on training deep neural networks show that AdaComm can take $3 \times$ less time than fully synchronous SGD, and still reach the same final training loss.

Adaptive Communication Strategies in Distributed SGD

The paper "Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD" by Jianyu Wang and Gauri Joshi explores a critical issue in distributed stochastic gradient descent (SGD): managing communication frequency to optimize computational efficiency without sacrificing convergence quality. The proposed adaptive communication strategy, known as AdaComm, offers a novel approach to modulating communication periods dynamically to enhance the training speed while minimizing error.

Context and Motivation

The increasing scale of machine learning training, particularly deep learning, necessitates distributed computing frameworks that partition the tasks across multiple computing nodes. This setup introduces variability due to node straggling and communication delays, which can impede rapid convergence. Traditional analyses of SGD primarily focus on error convergence with respect to iterations. However, real-world implementations demand optimization not just for the number of iterations but for the actual wall-clock time, integrating computation and communication delays.

Local-Update SGD and Communication Challenges

Local-update SGD frameworks allow worker nodes to perform updates locally before periodically averaging the models. This strategy significantly reduces communication costs compared to fully synchronous approaches. However, it also risks higher error convergence due to model discrepancies across nodes. The central challenge is balancing these trade-offs—optimizing the communication period ($\tau$), tackling the dual goals of rapid initial convergence without inflating the ultimate error floor.

AdaComm: An Adaptive Strategy

AdaComm presents a flexible approach where communication frequency starts low to save on initial communication costs and then increases as the model approaches convergence to lower the error floor. This strategy exploits the observation that initial large $\tau$ values accelerate error reduction but lead to higher terminal errors. By adaptively adjusting $\tau$, AdaComm ensures optimal error-runtime performance across different training phases.

Theoretical Foundation and Contributions

The paper provides a rigorous theoretical framework for understanding the runtime and error convergence of periodic-averaging SGD (PASGD). Leveraging runtime analysis, the authors derive optimal communication periods that minimize wall-clock error upper bounds. Noteworthy contributions include:

1. Runtime Analysis: By modeling local computation and communication times, the paper quantifies PASGD's runtime speed-up over fully synchronous SGD, highlighting the reduced impact of node stragglers.
2. Error Convergence Analysis: Building on prior convergence results, the paper presents a combined error-runtime analysis, offering new insights into optimal communication period selection at varying training stages.
3. Dynamic Communication Protocol: AdaComm dynamically updates $\tau$ based on training progress, achieving substantial runtime improvements in practical neural network training tasks.
4. Convergence Analysis for Variable $\tau$: The paper generalizes convergence proofs for PASGD, encompassing variable $\tau$ settings, thus extending practical guidelines for integrating adaptive learning rates.

Practical and Theoretical Implications

AdaComm aligns with practical needs in distributed SGD, as evidenced by empirical results on popular architectures like VGG-16 and ResNet-50. The adaptive strategy yields up to a threefold speedup compared to synchronous methods, maintaining comparable error floors. This improvement demonstrates significant implications for large-scale neural network training, where efficient resource utilization and time optimization are paramount.

Theoretically, AdaComm's framework sets a precedent for further exploration of adaptive mechanisms in distributed learning settings. Future research may build on these findings, exploring extensions to elastic-averaging, federated learning, and decentralized SGD frameworks.

Conclusion

In summary, "Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD" offers a well-substantiated advancement in distributed machine learning training. By diving into the intricacies of communication period dynamics, it provides both a practical tool and a theoretical basis for optimizing SGD in distributed systems. The implications extend beyond current implementations, promising advancements in computational speed and efficiency across a range of domains deploying large-scale learning models.