Automatic Between-Pulse Analysis of DIII-D Experimental Data Performed Remotely on a Supercomputer at Argonne Leadership Computing Facility. (18th August 2018)

• Record Type: Journal Article
• Authors: Kostuk, M.; Uram, T. D.; Evans, T.; Orlov, D. M.; Papka, M. E.; Schissel, D.
• Is Part Of: Fusion science and technology. Volume 74:Number 1/2(2018)
• Page Start: 135
• Page End: 143
• Publication Date: 2018-08-18
• Subjects: High-performance computing -- remote analysis -- real-time queue; Fusion reactors -- Periodicals; Nuclear fusion -- Periodicals; Fusion reactors; Nuclear fusion; Periodicals; 621.48405
• DOI: 10.1080/15361055.2017.1390388
• Languages: English
• ISSNs: 1536-1055

Abstract

Abstract: For the first time, an automatically triggered, between-pulse fusion science analysis code was run on-demand at a remotely located supercomputer at Argonne Leadership Computing Facility (ALCF, Lemont, Illinois) in support of in-process experiments being performed at DIII-D (San Diego, California). This represents a new paradigm for combining geographically distant experimental and high-performance computing facilities to provide enhanced data analysis that is quickly available to researchers. Enhanced analysis improves the understanding of the current pulse, translating into a more efficient use of experimental resources and quality of the resultant science. The analysis code used here, called SURFMN, calculates the magnetic structure of the plasma using Fourier transform. Increasing the number of Fourier components provides a more accurate determination of the stochastic boundary layer near the plasma edge by better resolving magnetic islands, but requires 26 min to complete using local DIII-D resources, putting it well outside the useful time range for between-pulse analysis. These islands relate to confinement and edge-localized mode suppression, and may be controlled by adjusting coil currents for the next pulse. ALCF has ensured on-demand execution of SURFMN by providing a reserved queue, a specialized service that launches the code after receiving an automatic trigger, and network access from the worker nodes for data transfer. Runs are executed on 252 cores of ALCF's Cooley cluster and the data are available locally at DIII-D within 3 min of triggering. The original SURFMN design limits additional improvements with more cores; however, our work shows a path forward where codes that benefit from thousands of processors can run between pulses.