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 2135-2146

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1109/TCSS.2023.3292932

Mer information

Senast uppdaterat

2024-04-20