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Image-based diagnostics are key for fusion experiments. The operating conditions at ITER and the future machines require changing the role of such systems from monitoring and archiving for offline postprocessing to real-time processing. One of the roles of such systems is machine protection. A relevant application of vision diagnostics is the wall and divertor temperature monitoring and hot spot detection. However, algorithms for hot spot detection are computationally costly. To achieve real-time performance at the required time resolution for all these experiments, evaluating and validating the newest technologies is vital. This work applies heterogeneous computing techniques based on the OpenCL standard to the real-time hot spot detection problem and obtains the performance values in a Micro Telecommunications Computer Architecture (MTCA) platform. OpenCL reduces the development time, improves portability, and simplifies the evaluation and validation of each part of the algorithm to find the best-suited device in the heterogeneous system. The proposed solution enables balancing the computational load between a field-programmable gate array (FPGA) and a graphical processing unit (GPU). The algorithm has been adapted and optimized, taking profit on the particularities of each platform.

Application of Heterogeneous Computing Techniques for the Development of an Image-Based Hot Spot Detection System Using MTCA