Building a scientific workflow framework to enable real‐time machine learning and visualization. (4th June 2018)

• Record Type: Journal Article
• Authors: Li, Feng; Song, Fengguang
• Other Names: Thota Abhinav guestEditor.; He Yun guestEditor.; Hart David guestEditor.; Dahan Maytal guestEditor.; Navratil Paul guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 16(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-04
• Subjects: computational fluid dynamics -- DataSpaces -- high performance computing -- machine learning -- real‐time online data analytics -- scientific workflows; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4703
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Nowadays, we have entered the era of big data. In the area of high performance computing, large‐scale simulations can generate huge amounts of data with potentially critical information. However, these data are usually saved in intermediate files and are not instantly visible until advanced data analytics techniques are applied after reading all simulation data from persistent storages (eg, local disks or a parallel file system). This approach puts users in a situation where they spend long time on waiting for running simulations while not knowing the status of the running job. In this paper, we build a new computational framework to couple scientific simulations with multi‐step machine learning processes and in‐situ data visualizations. We also design a new scalable simulation‐time clustering algorithm to automatically detect fluid flow anomalies. This computational framework is built upon different software components and provides plug‐in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real‐time notifications of special patterns or anomalies from ongoing extreme‐scale turbulent flow simulations.