A distributed approach for a COVID-19 fractional time-delay model | ||
| Computational Mathematics and Computer Modeling with Applications (CMCMA) | ||
| دوره 1، شماره 1، شهریور 2022، صفحه 95-104 اصل مقاله (264.43 K) | ||
| نوع مقاله: Regular paper | ||
| شناسه دیجیتال (DOI): 10.52547/CMCMA.1.1.95 | ||
| نویسنده | ||
| Mahdi Movahedian Moghaddam* | ||
| Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| 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 | ||
| کلیدواژهها | ||
| grained parallelism؛ distributed computing؛ domain-specific parallelism؛ fractional calculus؛ predictor-corrector method | ||
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آمار تعداد مشاهده مقاله: 287 تعداد دریافت فایل اصل مقاله: 121 |
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