MontePython: Implementing Quantum Monte Carlo using Python
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 met...
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| Published in | Computer physics communications Vol. 177; no. 10; pp. 799 - 814 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.11.2007
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4655 1879-2944 |
| DOI | 10.1016/j.cpc.2007.06.013 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Nilsen, Jon Kristian |
| Author_xml | – sequence: 1 givenname: Jon Kristian surname: Nilsen fullname: Nilsen, Jon Kristian email: j.k.nilsen@usit.uio.no organization: USIT, Postboks 1059 Blindern, N-0316 Oslo, Norway |
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| CitedBy_id | crossref_primary_10_1016_j_cpc_2012_08_021 crossref_primary_10_1088_1742_6596_331_7_072026 crossref_primary_10_1080_07474938_2011_553573 crossref_primary_10_1021_cr2001564 |
| Cites_doi | 10.1007/s10909-005-6308-7 10.1103/PhysRevA.68.033602 10.1103/PhysRevA.71.053610 10.1063/1.1699114 10.1063/1.443766 10.1016/S0167-8191(02)00163-1 10.1126/science.269.5221.198 10.1145/358407.358427 10.1103/PhysRevA.63.023602 |
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Oliphant, et al., SciPy: Open Source Scientific Tools for Python, 2001 – volume: 29 start-page: 69 year: 2003 end-page: 94 ident: bib017 article-title: Testing parallel random number generators publication-title: Parallel Comput. – year: 2001 ident: bib013 article-title: Quadratic diffusion Monte Carlo and pure estimators for atoms publication-title: Phys. Rev. A – year: 1981 ident: bib015 article-title: The Art of Computer Programming – volume: 269 start-page: 198 year: 1995 ident: bib006 article-title: Observation of Bose–Einstein condensation in a dilute atomic vapor publication-title: Science – volume: 26 start-page: 436 year: 2000 end-page: 461 ident: bib016 article-title: Sprng: A scalable library for pseudorandom number generation publication-title: ACM Trans. Math. Software – volume: 140 start-page: 181 year: 2005 end-page: 210 ident: bib005 article-title: Variational Monte Carlo for interacting electrons in quantum dots publication-title: J. 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Software doi: 10.1145/358407.358427 – volume: 63 year: 2001 ident: 10.1016/j.cpc.2007.06.013_bib008 article-title: Bose–Einstein condensation in trapped bosons: A variational Monte Carlo analysis publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.63.023602 – year: 2001 ident: 10.1016/j.cpc.2007.06.013_bib018 article-title: Mayavi: A free tool for CFD data visualization – year: 2002 ident: 10.1016/j.cpc.2007.06.013_bib014 – ident: 10.1016/j.cpc.2007.06.013_bib001 – ident: 10.1016/j.cpc.2007.06.013_bib002 – start-page: 269 year: 1997 ident: 10.1016/j.cpc.2007.06.013_bib011 article-title: Monte Carlo methods in quantum many-body theories – ident: 10.1016/j.cpc.2007.06.013_bib003 |
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| Title | MontePython: Implementing Quantum Monte Carlo using Python |
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