A distributed approach for a COVID-19 fractional time-delay model

After the spread of COVID-19, several attempts were made to model it mathematically. Due to the high power of fractional differential equation modeling, a time delay fractional model was presented for the modeling spread of COVID-19. The solution of these models is done by computer systems in several ways, including the fractional predictor-corrector method, which has many challenges. Among these challenges are execution time, scalability, and memory consumption. In previous research, the shared memory approach was presented to reduce the execution time challenge. Still, because of the challenges of scalability and memory consumption, a coarse-grained distributed approach was presented in this research. The results presented in this research have been compared with sequential approaches and shared memory. These results have been implemented based on the data announced by the city of Wuhan in 2019, and a speedup of 1.704 was achieved per execution on 1000 inputs