High-Performance Algorithms for Soft X-Ray Diagnostics Towards Future Fusion Reactors and Power Generation

Nuclear fusion represents a transformative solution for global energy systems, offering a carbon-free, inherently safe, and virtually inexhaustible power source. As the field transitions from experimental reactors like ITER to demonstration power plants (DEMO) capable of delivering net electricity to the grid (300–500 MW), the computational demands for plasma control have escalated. Modern fusion diagnostics, particularly soft X-ray (SXR) systems, generate massive data volumes that require high-throughput processing to ensure plasma stability and optimize energy gain. Recent breakthroughs in record-breaking plasma durations have further exposed the critical latency bottlenecks in traditional analytical workflows. This work addresses these challenges by introducing advanced computational strategies optimized towards next-generation reactors. Firstly, we present new data-processing algorithms in C++ and CUDA, achieving significant reductions in computation time. This allowed for more efficient analysis of collected experimental data for plasma confinement studies. Secondly, we discuss hardware architectures that will allow, in the future, up-scaling and parallel runtime processing of data with a feedback signal to the reactor control systems. We present a detailed analysis of the computational workflows underlying soft X-ray diagnostics, followed by a presentation of the proposed optimized algorithms. Their impact on prospective hardware system designs is then evaluated in terms of scalability, latency, and throughput. Performance evaluations demonstrated substantial speedups of both the sequential CPU-based and the parallel GPU-based algorithms, highlighting the potential of these methods for future real-time plasma control for energetically stable and efficient fusion power generation. The sequential and parallel algorithms were 18.8 and 89.1 times faster, respectively, versus the baseline implementation. The processing rate was increased from 31.8 MiB/s to 4.32 GiB/s. The results show the effectiveness of massively parallel computation for plasma diagnostics and pave the way towards further research to produce a cluster-based distributed system. The demand for such high-performance, real-time data processing methodologies extends beyond the plasma confinement domain and is expected to grow across energy systems as they become increasingly complex and data-driven.