As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

中文翻译：

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大型湍流模拟的原位数据压缩深度学习

随着湍流模拟的规模不断增长，对于可视化，分析和重新启动检查点，原位数据压缩变得越来越重要。对于这些应用，具有低计算和通信开销的单遍压缩技术至关重要。在本文中，我们提出了一种现场学习的深度学习方法使用针对三维湍流定制的自动编码器体系结构进行压缩，非常适合当代异构计算资源。针对三种不同的典型湍流，将自动编码器与最近引入的随机单程奇异值分解（SVD）进行了比较：衰减的均质各向同性湍流，泰勒-格林涡旋和湍流通道流。我们提出的全卷积自动编码器架构可通过单遍将湍流快照压缩64倍，可输入任意大小的输入字段，比典型模拟尺寸的随机单遍SVD便宜计算，在看不见的流配置下表现出色，并已公开发布。