Parallel algorithm for synthetic image generation with application to tokamak plasma diagnostics

Abstract

SummaryThe tomographic reconstruction is a powerful diagnostic tool in nuclear fusion experiments for the determination of the shape and position of the plasma. However, neural networks are emerging as a suitable alternative to conventional plasma tomography algorithms. In order to train such AI/ML based models, we need large‐scale, diversified image data for learning and evaluation which is difficult to obtain from real experiments. Accuracy and real‐time response is also critical, therefore in this article we propose an effective shared memory based parallel algorithm for synthetic imaging diagnostic data generation. The practicality of the proposed parallel algorithm has been evaluated experimentally by comparing it to the sequential algorithm on two different computing architectures. We observe a maximum speedup of 21 at 32 threads and demonstrate that our proposed parallel algorithm scales well over a range of image sizes. The proposed parallel algorithm can be used to obtain the synthetic images within a few seconds which is very important for real time applications. We also provide an analysis on the importance of choosing the right scheduling type and optimum chunk size to obtain the maximum speedup.