Large-Scale Parallelization of Human Migration Simulation
Artikel i vetenskaplig tidskrift, 2024
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.
parallelization
Behavioral sciences
Predictive models
Statistics
refugees
global challenges
computational complexity
high performance computing (HPC)
migration
Intel Xeon
Codes
Sociology
benchmarks
AMD Rome
Biological system modeling
modeling
Urban areas
global systems science (GSS)
Författare
D. Groen
Brunel University London
Nikela Papadopoulou
Chalmers, Data- och informationsteknik, Datorteknik
Petros Anastasiadis
National Technical University of Athens (NTUA)
Marcin Lawenda
Poznanskie Centrum Superkomputerowo Sieciowe
Lukasz Szustak
Poznanskie Centrum Superkomputerowo Sieciowe
Sergiy Gogolenko
Universität Stuttgart
Hamid Arabnejad
Brunel University London
Alireza Jahani
Brunel University London
IEEE Transactions on Computational Social Systems
2329924x (eISSN)
Vol. 11 2 2135-2146Ämneskategorier
Datavetenskap (datalogi)
DOI
10.1109/TCSS.2023.3292932