OpenCL Implementation of an Adaptive Disruption Predictor Based on a Probabilistic Venn Classifier

The ability and flexibility of the open computing language (OpenCL) for task parallelization in heterogeneous computing platforms [field-programmable gate array (FPGA), CPU, and GPU] are remarkable advantages when designing advanced data acquisition and processing systems. The use of FPGA devices in data acquisition devices increases the capabilities of traditional data acquisition systems, thus allowing the implementation of complex mathematical algorithms with the acquired data. This paper shows an OpenCL implementation of an adaptive probabilistic disruption predictor for fusion devices implemented in a Cyclone V SoC device with an ARM processor, the FPGA logic, and an analog to digital converter. This paper describes the methodology used, the hardware/software system architecture, and the implementation results. The work highlights the critical aspects involved in designing these OpenCL-based systems and, in particular, the implementation of the board support package. This paper also presents the aspects such as the significant differences in the design flow concept between FPGA and GPU OpenCL implementations and how to optimize the FPGA implementation with OpenCL. The results show that it is possible to make predictions with computation times shorter than $500~\mu \text{s}$ when using this low-cost SoC.