GPGPU application in fusion science

GPGPUs have firmly earned their reputation in HPC (High Performance Computing) as hardware for massively parallel computation. However their application in fusion science is quite marginal and not considered a mainstream approach to numerical problems. Computation advances have increased immensely over the last decade and continue to accelerate. GPGPU boards were always an alternative and exotic approach to problem solving and scientific programming, which was cultivated only by enthusiasts and specialized programmers. Today it is about 10 years, since the first fully programmable GPUs appeared on the market. And due to exponential growth in processing power over the years GPGPUs are not the alternative choice any more, but they became the main choice for big problem solving. Originally developed for and dominating in fields such as image and media processing, image rendering, video encoding/decoding, image scaling, stereo vision and pattern recognition GPGPUs are also becoming mainstream computation platforms in scientific fields such as signal processing, physics, finance and biology. This PhD contains solutions and approaches to two relevant problems for fusion and plasma science using GPGPU processing. First problem belongs to the realms of plasma and accelerator physics. I will present number of plasma simulations built on a PIC (Particle In Cell) method such as plasma sheath simulation, electron beam simulation, negative ion beam simulation and space charge compensation simulation. Second problem belongs to the realms of tomography and real-time control. I will present number of simulated tomographic plasma reconstructions of Fourier-Bessel type and their analysis all in real-time oriented approach, i.e. GPGPU based implementations are integrated into MARTe environment. MARTe is a framework for real-time application developed at JET (Joint European Torus) and used in several european fusion labs. These two sets of problems represent a complete spectrum of GPGPU operation capabilities. PIC based problems are large complex simulations operated as batch processes, which do not have a time constraint and operate on huge amounts of memory. While tomographic plasma reconstructions are online (realtime) processes, which have a strict latency/time constraints suggested by the time scales of real-time control and operate on relatively small amounts of memory. Such a variety of problems covers a very broad range of disciplines and fields of science: such as plasma physics, NBI (Neutral Beam Injector) physics, tokamak physics, parallel computing, iterative/direct matrix solvers, PIC method, tomography and so on. PhD thesis also includes an extended performance analysis of Nvidia GPU cards considering the applicability to the real-time control and real-time performance. In order to approach the aforementioned problems I as a PhD candidate had to gain knowledge in those relevant fields and build a vast range of practical skills such as: parallel/sequential CPU programming, GPU programming, MARTe programming, MatLab programming, IDL programming and Python programming