Design for parallel computation of model-based signal processing in Thomson scattering diagnostic

The Thomson Scattering (TS) diagnostic system calculates electron temperature and density profiles of tokamak plasma. The diagnostic is enabled by measuring and analyzing pulse signals of scattered laser light in the tokamak plasma. In order to improve accuracy of the diagnostic, it is necessary to reduce noise level of the pulse signals. In KSTAR, a fast digitizer with 5 GS/s has been adopted for obtaining the pulse shape information, and a research of model-based signal processing for the pulse signals has been conducted. In this paper, we present a new approach for the model-based signal processing and its parallel computation architecture design to improve its computation speed. As our approach, we conduct a nonlinear least square method in frequency domain by taking Fourier Transformation (FT) of the TS pulse model containing a convolution operator. Then, we can obtain algebraic functions for the transformed model and its Jacobian, which can reduce amount of computation and can be computed in parallel respect to frequency component. We implement the parallel computation of the algorithm in Graphical Processing Unit (GPU) and evaluate its effectiveness in terms of computation performance, convergence analysis, and variation of the model parameters.