Authors: Naw Safrin Sattar (University of New Orleans) and Ying-Wai Li (Los Alamos National Laboratory)
Abstract: Monte Carlo (MC) simulations are important tools for studying thermodynamics and materials properties at finite temperature. Scaling up to larger systems allows for simulations comparable to experimental length scale. In this work, we focus on improving the weak scaling performance to simulate large magnetic spin systems up to millions of atoms. We parallelize the computation of physical observables within a single random walker using checkerboard domain decomposition implemented with OpenMP and compare the performance of the same code base on different many-core processors. We analyze the effects of thread affinity and hyper-threading on the runtime performance and achieve 14x/12x/6x speedups for Arm/Intel/AMD architectures respectively for the largest system we report in this work (about 4.2 millions spins). This study is helpful in informing end-users on the choice of the best multi-core architecture suited to their needs for similar pattern of parallel applications.
Best Poster Finalist (BP): no
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