C implementation of the determinant quantum Monte Carlo (DQMC) method applied to Hubbard-type models.
The code depends on the CBLAS and LAPACKE libraries. These can be installed via sudo apt install libblas-dev liblapacke-dev
(on Ubuntu Linux) or similar. Alternatively, the Makefile shows how to use the Intel compiler with MKL.
Call make
to build the project. You might have to adapt some parameters in the Makefile beforehand (see the comments there).
To run the code, cd into the bin subfolder and call hubbard_dqmc <paramfile>
; some example parameter files are provided there. For unit testing, cd into the test subfolder and execute run_tests
.
The Mathematica unit test notebooks can be opened by Mathematica or the free CDF player.
Copyright (c) 2015-2017, Edwin Huang and Christian B. Mendl
This code was developed at Stanford University with support from the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, under Contract No. DE-AC02-76SF00515.
- R. Blankenbecler, D. J. Scalapino, R. L. Sugar
Monte Carlo calculations of coupled boson-fermion systems. I
Phys. Rev. D 24, 2278 (1981) DOI - S. R. White, D. J. Scalapino, R. L. Sugar, E. Y. Loh, J. E. Gubernatis, and R. T. Scalettar
Numerical study of the two-dimensional Hubbard model
Phys. Rev. B 40, 506-516 (1989) DOI - Z. Bai, C.-R. Lee, R.-C. Li, S. Xu
Stable solutions of linear systems involving long chain of matrix multiplications
Linear Algebra Appl. 435, 659-673 (2011) DOI - A. Tomas, C.-C. Chang, R. Scalettar, Z. Bai
Advancing large scale many-body QMC simulations on GPU accelerated multicore systems
IEEE 26th International Parallel & Distributed Processing Symposium (IPDPS) 308-319 (2012) DOI - S. Gogolenko, Z. Bai, R. Scalettar
Structured orthogonal inversion of block p-cyclic matrices on multicore with GPU accelerators
Euro-Par 2014 Parallel Processing, LNCS 8632, pages 524-535 (2014) DOI - C. Jiang, Z. Bai, R. Scalettar
A fast selected inversion algorithm for Green's function calculation in many-body quantum Monte Carlo simulations
IEEE International Parallel and Distributed Processing Symposium, 2016 DOI