h-分析和数据并行物理信息的神经网络
Paul Escapil-Inchauspé1,2, Gonzalo A Ruz3,4,5
1Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile. paul.escapil@edu.uai.cl.
Scientific reports
|October 16, 2023
概括
我们介绍了一种加速基于物理的机器学习 (PIML) 的新方法,使用多个GPU的数据并行. 这种方法提高了用于复杂模拟的PIML模型的效率和可扩展性.
科学领域:
- 计算科学与工程 计算科学与工程
- 机器学习 机器学习
- 数字分析 数字分析
背景情况:
- 基于物理的机器学习 (PIML) 和基于物理的神经网络 (PINNs) 是科学模拟的强大工具.
- 将PIML模型扩展到处理大型数据集的复杂,高维问题是计算上具有挑战性的.
- 现有的方法可能在复杂的现实应用中难以获得效率和稳定性.
研究的目的:
- 开发一个具有可扩展性和高通量协议,用于PIML系统的数据并行加速.
- 在多个图形处理单元 (GPU) 上研究物理信息神经网络 (PINNs) 的实现和效率.
- 在加速的PIML中为概括错误和火车测试差距提供新的理论收界限.
主要方法:
- 使用Horovod培训框架将h分析与数据并行加速相结合的新协议.
- 在多个GPU架构上实现协议.
- 为一般化错误和列车测试差距引出新的收界限.
主要成果:
- 拟议的数据并行加速协议很容易实施,不会影响培训准确性.
- 该方法表现出高效率和可控性,使得PIML具有可扩展性和稳定性.
- 广泛的数值实验证实了这种方法在越来越复杂的情况下的稳定性和一致性.
结论:
- 数据并行加速为通用,规模稳定的PIML提供了可行和高效的途径.
- 霍罗沃德框架为多GPU系统的直接实施提供了便利.
- 这项工作为使用PIML的先进真实世界模拟开辟了新的可能性.
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