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为了加速分布式深度学习系统中的模型并行性
Hyeonseong Choi1, Byung Hyun Lee2, Se Young Chun2,3
1Department of Computer Engineering, Korea Aerospace University, Goyang, South Korea.
PloS one
|November 2, 2023
概括
在多个GPU上训练大型深度神经网络是通过找到最佳的微批量大小来实现高效的管道和使用适当的规范化技术来实现模型和数据并行性来优化.
科学领域:
- * 深度学习 (Deep Learning) 是一种深度学习.
- * 分布式系统 * 分布式系统
- * * 高性能计算 高性能计算
背景情况:
- *由于模型和数据大小,训练大型深度神经网络 (DNN) 通常需要多个GPU.
- * 模型并行性,将模型分成GPU,由于通信开销,面临可扩展性和效率方面的挑战.
- * 低效的管道和规范化方法可能会阻碍分布式训练中的GPU利用率和模型准确性.
研究的目的:
- * 调查多个GPU分布式DNN训练的高效管道和规范化技术.
- * 解决模型并行性的挑战,旨在最大限度地利用GPU并保持模型的准确性.
- * 为了使大型模型与大型迷你批量进行训练,而不会影响性能.
主要方法:
- * 开发了一种新的算法,以寻找最佳的微批量大小,根据GPU数量和模型并行性的内存量身定制.
- *研究了不同规范化方法 (批量规范化,组规范化) 对分布式训练表现的影响.
- * 进行实验,比较拟议的方法与常规模型并行性,以提高效率和准确性.
主要成果:
- * 拟议的微批量大小搜索算法将图像吞吐量提高了高达12%,可训练的微批量大小增加了25%.
- * 分享批量信息提高了数据并行性中的批量规范化性能.
- * 组规范化将管道模型并行性中的准确性降低到最低,并确保在各种小型批量大小中保持一致的准确性.
结论:
- *最佳的微批量大小对于高效的管道和最大限度地利用模型并行性中的GPU至关重要.
- * 组规范化在管道模型并行过程中有效地减轻了准确性损失.
- * 该研究提供了增强分布式深度学习培训可扩展性和准确性的实际解决方案.
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