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

► Tokamak magnetic field lines are simulated on a GPU. ► Numerical integration of a set of nonlinear differential equations is required. ► Using the GPU yields a significant reduction in processing time compared to the CPU. ► Computational runs that took days now take hours. ► These gains have been...

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Bibliographic Details
Published in:Fusion engineering and design 2011-06, Vol.86 (4), p.399-406
Main Authors: Kalling, R.C., Evans, T.E., Orlov, D.M., Schissel, D.P., R.Maingi, Menard, J.E., Sabbagh, S.A.
Format: Article
Language:English
Subjects:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
Applied sciences
Computation
COMPUTER CODES
Computer simulation
Controled nuclear fusion plants
DIFFERENTIAL EQUATIONS
Energy
Energy. Thermal use of fuels
Exact sciences and technology
Expenses
FLEXIBILITY
FORTRAN
General-purpose computation on graphical processing units
Graphical processing unit
IMPLEMENTATION
Installations for energy generation and conversion: thermal and electrical energy
MAGNETIC FIELDS
MAGNETIC ISLANDS
Mathematical models
Non-axisymmetric magnetic perturbations
Nonlinearity
NSTX
PLASMA INSTABILITY
Reduction
SIMULATION
Stochastic magnetic field lines
Tokamaks
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Summary:► Tokamak magnetic field lines are simulated on a GPU. ► Numerical integration of a set of nonlinear differential equations is required. ► Using the GPU yields a significant reduction in processing time compared to the CPU. ► Computational runs that took days now take hours. ► These gains have been accomplished without significant hardware expense. 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.
ISSN:0920-3796
1873-7196
DOI:10.1016/j.fusengdes.2011.03.064