Li, FengSong, Fengguang2018-04-062018-04-062017-07Li, F., & Song, F. (2017, July). A Real-Time Machine Learning and Visualization Framework for Scientific Workflows. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (p. 3). ACM.https://hdl.handle.net/1805/15780High-performance computing resources are currently widely used in science and engineering areas. Typical post-hoc approaches use persistent storage to save produced data from simulation, thus reading from storage to memory is required for data analysis tasks. For large-scale scientific simulations, such I/O operation will produce significant overhead. In-situ/in-transit approaches bypass I/O by accessing and processing in-memory simulation results directly, which suggests simulations and analysis applications should be more closely coupled. This paper constructs a flexible and extensible framework to connect scientific simulations with multi-steps machine learning processes and in-situ visualization tools, thus providing plugged-in analysis and visualization functionality over complex workflows at real time. A distributed simulation-time clustering method is proposed to detect anomalies from real turbulence flows.enIUPUI Open Access Policymachine learningscientific workflowsvisualization frameworkArticle