Parallelisation of decision-making techniques in aquaculture enterprises

Nowadays, the Artificial Intelligent (AI) techniques are applied in enterprise software to solve Big Data and Business Intelligence (BI) problems. But most AI techniques are computationally excessive, and they become unfeasible for common business use. Therefore, specific high performance computing is needed to reduce the response time and make these software applications viable on an industrial environment. The main objective of this paper is to demonstrate the improvement of an aquaculture BI tool based in AI techniques, using parallel programming. This tool, called AquiAID, was created by the research group of Economic Management for the Sustainable Development of Primary Sector of the Universidad de Cantabria. The parallelisation reduces the computation time up to 60 times, and the energy efficiency by 600 times with respect to the sequential program. With these improvements, the software will improve the fish farming management in aquaculture industry.