Scalable Metropolis Monte Carlo for simulation of hard shapes

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• 作者：Joshua A. Anderson^a ; M. Eric Irrgang^b ; Sharon C. Glotzer^a ; ^b ; ^{sglotzer@umich.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Monte Carlo ; Hard particle ; GPU
• 刊名：Computer Physics Communications
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：204
• 期：Complete
• 页码：21-30
• 全文大小：630 K

文摘

We design and implement a scalable hard particle Monte Carlo simulation toolkit (HPMC), and release it open source as part of HOOMD-blue. HPMC runs in parallel on many CPUs and many GPUs using domain decomposition. We employ BVH trees instead of cell lists on the CPU for fast performance, especially with large particle size disparity, and optimize inner loops with SIMD vector intrinsics on the CPU. Our GPU kernel proposes many trial moves in parallel on a checkerboard and uses a block-level queue to redistribute work among threads and avoid divergence. HPMC supports a wide variety of shape classes, including spheres/disks, unions of spheres, convex polygons, convex spheropolygons, concave polygons, ellipsoids/ellipses, convex polyhedra, convex spheropolyhedra, spheres cut by planes, and concave polyhedra. NVT and NPT ensembles can be run in 2D or 3D triclinic boxes. Additional integration schemes permit Frenkel–Ladd free energy computations and implicit depletant simulations. In a benchmark system of a fluid of 4096 pentagons, HPMC performs 10 million sweeps in 10 min on 96 CPU cores on XSEDE Comet. The same simulation would take 7.6 h in serial. HPMC also scales to large system sizes, and the same benchmark with 16.8 million particles runs in 1.4 h on 2048 GPUs on OLCF Titan.