Accelerating the numerical simulation of magnetic field lines in tokamaks using the GPU

    Research output: Contribution to journalArticlepeer-review

    13 Scopus citations

    Abstract

    trip3d is a field line simulation code that numerically integrates a set of nonlinear magnetic field line differential equations. The code is used to study properties of magnetic islands and stochastic or chaotic field line topologies that are important for designing non-axisymmetric magnetic perturbation coils for controlling plasma instabilities in future machines. The code is very computationally intensive and for large runs can take on the order of days to complete on a traditional single CPU. This work describes how the code was converted from Fortran to C and then restructured to take advantage of GPU computing using NVIDIA's CUDA. The reduction in computing time has been dramatic where runs that previously took days now take hours allowing a scale of problem to be examined that would previously not have been attempted. These gains have been accomplished without significant hardware expense. Performance, correctness, code flexibility, and implementation time have been analyzed to gauge the success and applicability of these methods when compared to the traditional multi-CPU approach.

    Original languageEnglish (US)
    Pages (from-to)399-406
    Number of pages8
    JournalFusion Engineering and Design
    Volume86
    Issue number4-5
    DOIs
    StatePublished - Jun 2011

    All Science Journal Classification (ASJC) codes

    • Civil and Structural Engineering
    • Nuclear Energy and Engineering
    • General Materials Science
    • Mechanical Engineering

    Keywords

    • General-purpose computation on graphical processing units
    • Graphical processing unit
    • Non-axisymmetric magnetic perturbations
    • NSTX
    • Stochastic magnetic field lines
    • Tokamaks

    Fingerprint

    Dive into the research topics of 'Accelerating the numerical simulation of magnetic field lines in tokamaks using the GPU'. Together they form a unique fingerprint.

    Cite this