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arXiv physics/9710028physics.comp-ph
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自适应MC积分器的简化

Parallelization of adaptive MC Integrators

Richard Kreckel

摘要:

数值积分的蒙特卡罗(MC)方法初看起来似乎是令人尴尬的并行。然而,当应用自适应方案以增强收敛时,在每个处理器上复制整个作业的看似最自然的方式可能会破坏自适应行为。以流行的VEGAS算法为例,提出了一种经济的变粒度半微并行化方法,并与另一种直接的宏并行化方法进行了比较。这种半微并行化的一个可移植的实现被用在xloops项目中,并且是公开的。

英文摘要:

Monte Carlo (MC) methods for numerical integration seem to be embarassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS-Algorithm as an example an economic method of semi-micro parallelization with variable grain-size is presented and contrasted with another straightforward approach of macro-parallelization. A portable implementation of this semi-micro parallelization is used in the xloops-project and is made publicly available.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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