In this paper a distributed-computing technique to extract photovoltaic module parameters is presented. The proposed procedure is based on a particle swarm optimization (PSO) strategy that exploits interlacing of the experimental data in order to decrease the computational load, and parallel computing of different and independent runs to collect a large amount of data in order to save computational time. The results show that the choice of the search range width and the rules to accept/refuse possible solutions strongly influences the parameter extraction and involves exploiting further statistical analysis to control the statistical instability typical of numerical data fitting algorithms.