Large-Scale Parallelization of Human Migration Simulation

• Published in: IEEE transactions on computational social systems 2024-04, Vol.11 (2), p.2135-2146
• Main Authors: Groen, Derek, Papadopoulou, Nikela, Anastasiadis, Petros, Lawenda, Marcin, Szustak, Lukasz, Gogolenko, Sergiy, Arabnejad, Hamid, Jahani, Alireza
• Format: Article
• Language: English
• Subjects: Agent-based models; Algorithms; AMD Rome; Behavioral sciences; benchmarks; Biological system modeling; Codes; computational complexity; Displaced persons; global challenges; global systems science (GSS); Globalization; high performance computing (HPC); Intel Xeon; Migration; modeling; parallelization; Predictive models; refugees; Simulation; Sociology; Statistics; Urban areas
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2023.3292932

Forced displacement of people worldwide, for example, due to violent conflicts, is common in the modern world, and today more than 82 million people are forcibly displaced. This puts the problem of migration at the forefront of the most important problems of humanity. The Flee simulation code is an agent-based modeling tool that can forecast population displacements in civil war settings, but performing accurate simulations requires nonnegligible computational capacity. In this article, we present our approach to Flee parallelization for fast execution on multicore platforms, as well as discuss the computational complexity of the algorithm and its implementation. We benchmark parallelized code using supercomputers equipped with AMD EPYC Rome 7742 and Intel Xeon Platinum 8268 processors and investigate its performance across a range of alternative rule sets, different refinements in the spatial representation, and various numbers of agents representing displaced persons. We find that Flee scales excellently to up to 8192 cores for large cases, although very detailed location graphs can impose a large initialization time overhead.