MontePython: Implementing Quantum Monte Carlo using Python

• Published in: Computer physics communications Vol. 177; no. 10; pp. 799 - 814
• Main Author: Nilsen, Jon Kristian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2007
• Subjects: Bose–Einstein condensation; C++; MPI; Python; Quantum Monte Carlo; 03.75.Hh; 03.75.Lm; C++; Bose–Einstein condensation; MPI; Quantum Monte Carlo; Python
• ISSN: 0010-4655; 1879-2944
• DOI: 10.1016/j.cpc.2007.06.013

We present a cross-language C++/Python program for simulations of quantum mechanical systems with the use of Quantum Monte Carlo (QMC) methods. We describe a system for which to apply QMC, the algorithms of variational Monte Carlo and diffusion Monte Carlo and we describe how to implement theses methods in pure C++ and C++/Python. Furthermore we check the efficiency of the implementations in serial and parallel cases to show that the overhead using Python can be negligible. Program title: MontePython Catalogue identifier: ADZP_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADZP_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 49 519 No. of bytes in distributed program, including test data, etc.: 114 484 Distribution format: tar.gz Programming language: C++, Python Computer: PC, IBM RS6000/320, HP, ALPHA Operating system: LINUX Has the code been vectorised or parallelized?: Yes, parallelized with MPI Number of processors used: 1–96 RAM: Depends on physical system to be simulated Classification: 7.6; 16.1 Nature of problem: Investigating ab initio quantum mechanical systems, specifically Bose–Einstein condensation in dilute gases of 87Rb Solution method: Quantum Monte Carlo Running time: 225 min with 20 particles (with 4800 walkers moved in 1750 time steps) on 1 AMD Opteron TM Processor 2218 processor; Production run for, e.g., 200 particles takes around 24 hours on 32 such processors.