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[Licence| Download | New Version Template] aeup_v1_0.tar.gz(1194 Kbytes)
Manuscript Title: Fast quantum Monte Carlo on a GPU
Authors: Yaroslav Lutsyshyn
Program title: QL
Catalogue identifier: AEUP_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 187(2015)162
Programming language: CUDA-C, C, Fortran.
Computer: PC Intel i5, Xeon cluster, GPUs GTX 560Ti, Fermi M2090, Tesla K20.
Operating system: Linux.
RAM: Typical execution uses as much RAM as is available on the GPU; usually between 1 GB and 12 GB. Minimal requirement is 1 MB.
Keywords: GPU, CUDA, QMC, Quantum Monte Carlo, Variational Monte Carlo, Quantum liquid, Liquid 4He.
Classification: 4.12, 7.7.

Nature of problem:
QL package executes variational Monte Carlo for liquid helium-4 with Aziz II interaction potential and a Jastrow pair product trial wavefunction. Sampling is performed with a Metropolis scheme applied to single-particle updates. With minimal changes, the package can be applied to other bosonic fluids, given a pairwise interaction potential and a wavefunction in the form of a product of one- and two-body correlation factors.

Solution method:
The program is parallelized for execution with Nvidia GPU. By design, the generation of new configurations is performed with shared memory persistence and the asynchronous execution allows for the CPU load masking.

Restrictions:
Code is limited to variational Monte Carlo. Due to the limitation of the shared memory of GPU, only systems under 2000 particles can be treated on the Fermi generation cards, and up to 10000 on Kepler cards.

Running time:
Because of the statistical nature of Monte Carlo calculations, computations may be chained indefinitely to improve statistical accuracy. As an example, using the QL package, the energy of a liquid helium system with 1952 atoms can be computed to within 1mK per atom in less than 20 minutes. This corresponds to the relative error of 10-4. It is unlikely that a higher accuracy may be needed.