人工科学家——等离子体模拟的传输中机器学习
The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations
AI总结：
本文提出一种将等离子体模拟数据直接流式传输到机器学习框架的传输中学习工作流，绕过文件系统瓶颈，并以PIConGPU模拟开尔文-亥姆霍兹不稳定性为例，结合经验回放实现持续学习，验证了其在Frontier超算上的可行性。
摘要：
随着HPC集群规模不断扩大，且大规模模拟每次运行可产生拍字节级数据，分析工作面临巨大的IO和存储挑战。尤其是基于深度学习的技术，会利用这些海量领域数据来提取有助于建立科学理解的模式。在此，我们展示了一种流式工作流，其中模拟数据被直接流式传输到机器学习（ML）框架，从而绕过文件系统瓶颈。数据在传输过程中被转换，并且与模拟和模型训练异步进行。借助所提出的工作流，数据操作可以在常用且易于使用的编程语言中执行，使应用用户无需调整应用输出例程。作为概念验证，我们考虑了一个GPU加速的粒子网格（PIConGPU）模拟，模拟对象为开尔文-亥姆霍兹不稳定性（KHI）。我们采用经验回放，以避免在持续学习这一非稳态过程时发生灾难性遗忘。我们详细说明了在移植并扩展到Frontier百亿亿次系统过程中所解决的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
