动态加权策略缓解分布式训练中节点失效问题
A Dynamic Weighting Strategy to Mitigate Worker Node Failure in Distributed Deep Learning
- 根据节点状态动态调整权重，减少故障节点影响
- 实验显示收敛速度提升，测试性能更优
- 适合大规模分布式深度学习场景使用
深度学习模型复杂度提升与海量数据处理需求，使得大规模分布式系统在高效训练中变得至关重要。然而，这类系统面临通信开销大、硬件限制和节点故障等挑战。本文研究了分布式深度学习中的多种优化技术，包括弹性平均SGD（EASGD）和二阶方法AdaHessian。提出一种动态加权策略，以缓解因节点失效导致的慢速节点（straggler）问题，从而提升整体训练性能与效率。通过在不同工作节点数量和通信周期下的实验，验证了该策略能有效改善收敛速率和测试表现。
原文摘要 · Abstract (English)
The increasing complexity of deep learning models and the demand for processing vast amounts of data make the utilization of large-scale distributed systems for efficient training essential. These systems, however, face significant challenges such as communication overhead, hardware limitations, and node failure. This paper investigates various optimization techniques in distributed deep learning, including Elastic Averaging SGD (EASGD) and the second-order method AdaHessian. We propose a dynamic weighting strategy to mitigate the problem of straggler nodes due to failure, enhancing the performance and efficiency of the overall training process. We conduct experiments with different numbers of workers and communication periods to demonstrate improved convergence rates and test performance using our strategy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。