Fast quantum Monte Carlo on a GPU

Abstract

We present a scheme for the parallelization of quantum Monte Carlo on graphical processing units, focusing on bosonic systems and variational Monte Carlo. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent acceleration. Comparing with single core execution, GPU-accelerated code runs over x100 faster. The CUDA code is provided along with the package that is necessary to execute variational Monte Carlo for a system representing liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the latest Kepler architecture K20 GPU. Kepler-specific optimization is discussed.

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References in corpus (4)

• Continuum variational and diffusion quantum Monte Carlo calculations
• Real time demonstration of high bitrate quantum random number generation with coherent laser light
• Heavy-tailed random error in quantum Monte Carlo
• Optimum and efficient sampling for variational quantum Monte Carlo

Cited by in corpus (7)

• Simulation and understanding of quantum crystals
• Computing partial traces and reduced density matrices
• GPU-accelerated algorithms for many-particle continuous-time quantum walks
• Weakly parametrized Jastrow ansatz for a strongly correlated Bose system
• GPU acceleration of ab initio simulations of large-scale identical particles based on path integral molecular dynamics
• A coordinated wavefunction for the ground state of liquid helium-4
• Quantum Phase Transition with a Simple Variational Ansatz