Parallelized Quantum Monte Carlo Algorithm with Nonlocal Worm Updates
Parallelized Quantum Monte Carlo Algorithm with Nonlocal Worm Updates
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具有非局部蠕虫更新的并行量子蒙特卡罗算法
DOI：
10.1103/physrevlett.112.140603
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Naoki Kawashima
中科院分区：
文献类型：
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作者：
Akiko Masaki;Takafumi Suzuki;Kenji Harada;Synge Todo;Naoki Kawashima
基于路径积分表示中的蠕虫算法，我们提出了一种通用量子蒙特卡罗算法，适合通过域分解在分布式内存计算机上并行化。特别重要的是它在玻色子和自旋的大型晶格系统中的应用。引入大量蠕虫，其数量由虚构的横向场控制。作为基准，我们使用 3200 个计算核心研究了硬核 Bose-Hubbard 模型的 Bose-condensation 阶数参数的大小依赖性，显示出良好的并行化效率。
Based on the worm algorithm in the path-integral representation, we propose a general quantum Monte Carlo algorithm suitable for parallelizing on a distributed-memory computer by domain decomposition. Of particular importance is its application to large lattice systems of bosons and spins. A large number of worms are introduced and its population is controlled by a fictitious transverse field. For a benchmark, we study the size dependence of the Bose-condensation order parameter of the hard-core Bose-Hubbard model with, using 3200 computing cores, which shows good parallelization efficiency.