通过基于节点的通信来增强分布式神经网络训练
IEEE transactions on neural networks and learning systems
|September 18, 2023
概括
本研究介绍了基于节点的优化方法,以减少深度神经网络 (DNN) 中的梯度通信,显著减少训练时间并提高分布式设置中的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现代深度神经网络 (DNN) 需要大量的数据,导致大量的计算需求.
- 数据并行化策略虽然提高了运行时间,但由于广泛的梯度通信而面临瓶.
- 分布式平台中的通信延迟显著影响了DNN培训的效率.
研究的目的:
- 开发和介绍基于节点的优化步骤,以减少DNN中的梯度交换.
- 为各种通用DNN算法创建适用于各种通用DNN算法的通用通信方案.
- 解决阻碍分布式平台性能的通信延迟问题.
主要方法:
- 实现基于节点的优化,以最大限度地减少模型复制品之间的梯度交换.
- 开发了一个通信方案,考虑每个复制品在分布式平台中的位置.
- 通过使用各种神经网络架构,数据集和应用类型来评估该提案.
主要成果:
- 显著减少了深度神经网络的全球培训时间.
- 在模型准确度方面取得了轻微的改进.
- 在不同场景中验证了拟议的通信方案的稳定性和多功能性.
结论:
- 提出的基于节点的优化有效地减少了DNN培训中的梯度通信瓶.
- 多功能通信方案提高了分布式深度学习的效率和准确性.
- 这种方法为提高大规模DNN的性能提供了一个实际的解决方案.
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