Design of GPU-based parallel computation architecture of Thomson scattering diagnostic in KSTAR

Abstract

The Thomson scattering (TS) System is a diagnostic system to measure electron temperature and density profiles of tokamak plasma. The TS system requires measurement of many input signals, and the amount of raw data has significantly increased since the TS data acquisition (DAQ) system was upgraded to a fast digitizer. Research has been done on applying artificial neural network (ANN) to TS data analysis for reducing calculation time. In this paper, we propose a design of computation architecture to effectively process the increased amount of data caused by the fast digitizer and to maximize the computation performance of the ANN. In the design, the intensity values of each input signal and the ANNs can be computed in parallel by utilizing a graphical processing unit (GPU). Furthermore, we integrate the data analysis task into the TS DAQ program for real time operation. Considering stability of the integration, we separate tasks of the data acquisition and the data analysis into each thread operation, and make tasks of each digitizer board to be conducted in parallel. In the feasibility test of the design, the calculation time is shown to be appropriate for real time operation.