边运行等离子体模拟边用机器学习实时分析，避免存储瓶颈。
The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations
- 数据流直接输入模型，异步转换，跳过文件系统瓶颈。
- 在非稳态的卡伦-赫尔姆霍兹不稳定性模拟中持续学习，避免遗忘。
- 适配前沿超算系统，支持大规模模拟的实时分析。
随着高性能计算集群规模扩大，单次大规模模拟产生海量数据（每轮达拍字节级），带来严重的输入输出与存储挑战。深度学习技术可利用这些数据提取模式，促进科学理解。本文展示一种流式工作流：将模拟数据直接流至机器学习框架，绕过文件系统瓶颈。数据在传输过程中被异步处理，与模拟和模型训练并行。该流程支持使用通用编程语言进行数据操作，用户无需修改应用输出逻辑。以GPU加速的粒子-网格（PIConGPU）模拟卡伦-赫尔姆霍兹不稳定性为例，采用经验回放机制实现对非稳态过程的持续学习，避免灾难性遗忘。文章还详述了在前沿（Frontier）exascale系统上移植与扩展所面临的挑战。
原文摘要 · Abstract (English)
Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstrate a streaming workflow in which simulation data is streamed directly to a machine-learning (ML) framework, circumventing the file system bottleneck. Data is transformed in transit, asynchronously to the simulation and the training of the model. With the presented workflow, data operations can be performed in common and easy-to-use programming languages, freeing the application user from adapting the application output routines. As a proof-of-concept we consider a GPU accelerated particle-in-cell (PIConGPU) simulation of the Kelvin- Helmholtz instability (KHI). We employ experience replay to avoid catastrophic forgetting in learning from this non-steady process in a continual manner. We detail challenges addressed while porting and scaling to Frontier exascale system.
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