ZMCintegral: A package for multi-dimensional Monte Carlo integration on multi-GPUs

• Published in: Computer physics communications 2020-03, Vol.248, p.106962, Article 106962
• Main Authors: Wu, Hong-Zhong, Zhang, Jun-Jie, Pang, Long-Gang, Wang, Qun
• Format: Article
• Language: English
• Subjects: Heuristic tree search; Monte Carlo integration; Numba; Ray; Stratified sampling; Tensorflow
• ISSN: 0010-4655; 1879-2944
• DOI: 10.1016/j.cpc.2019.106962

We have developed a Python package ZMCintegral for multi-dimensional Monte Carlo integration on multiple Graphics Processing Units (GPUs). The package employs a stratified sampling and heuristic tree search algorithm. We have built three versions of this package: one with Tensorflow and other two with Numba, and both support general user defined functions with a user-friendly interface. We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work. The precision and speed of our package is compared with that of VEGAS for two typical integrands, a 6-dimensional oscillating function and a 9-dimensional Gaussian function. The results show that the speed of ZMCintegral is comparable to that of the VEGAS with a given precision. For heavy calculations, the algorithm can be scaled on distributed clusters of GPUs. Program Title: ZMCintegral Program Files doi:http://dx.doi.org/10.17632/p7wc7k6mpp.1 Licensing provisions: Apache License Version 2.0 Programming language: Python External routines/libraries: Tensorflow; Numba; Ray Nature of problem: Easy to use python package for Multidimensional-multi-GPUs Monte Carlo integration Solution method: Stratified sampling and heuristic tree search using multiple GPUs on distributed clusters