Awesome
GPUQT
An efficient implementation of linear scaling quantum transport (LSQT) methods which supports both pure CPU and GPU+CPU computations. This code can be used to obtain charge and spin transport properties of large systems described by a real-space tight-binding Hamiltonian.
The major features of GPUQT have been integrated into the more general GPUMD package (https://github.com/brucefan1983/GPUMD). Within the molecular dynamics (MD) framework, one can combine the electron and ion motions to incorporate electron-ion scattering, which is absent from the current package. The current GPUQT package will not be further developed.
Prerequisites
- To use the CPU version, it only requires a
g++
compiler and themake
program. - To use the GPU version, it also requires a CUDA-enabled GPU with compute capability of 3.5 or higher and a
CUDA
toolkit. I have tested the GPU version on both Windows (requires thecl.exe
compiler from MSVC and amake.exe
program which can be downloaded here) and Linux systems.
Installing
- Go to
src
and- type
make -f makefile.cpu
to build the CPU version. This will produce an executable calledlsqt_cpu
in thesrc
folder. - type
make -f makefile.gpu
to build the GPU version. This will produce an executable calledlsqt_gpu
in thesrc
folder.
- type
Running the examples
-
Edit the file
examples/input.txt
to include the paths (relative or absolute) of the working directories containing the examples you want to run. -
Go to the main folder where you can see the
src
folder and type one of the following commands:src/lsqt_gpu examples/input.txt
src/lsqt_cpu examples/input.txt
-
The results will be written into the output files (with suffix
.out
) in the working directories specified inexamples/input.txt
. If you run a simulation multiple times, new data will be appended to the existing output files.
Analyzing the results
Go to the working directories and run the MATLAB
scripts we have prepared. After getting familiar with the output files, one can analyze the results using her/his favorite computer language(s).
Authors
-
Zheyong Fan (Aalto University; brucenju(at)gmail.com; active developer): Wrote the first working version of this code.
-
Ville Vierimaa (Aalto University; not an active developer any more): Changed the code from the original C style to the current
C++
style and made many other improvements. -
Ari Harju (Aalto University; not an active developer any more): The supervisor of this project.
References
The most original paper on this method is:
- [1] S. Roche and D. Mayou, Conductivity of Quasiperiodic Systems: A Numerical Study, Phys. Rev. Lett. 79, 2518 (1997). https://doi.org/10.1103/PhysRevLett.79.2518
The major reference for the CUDA implementation is
- [2] Z. Fan, A. Uppstu, T. Siro, and A. Harju, Efficient linear-scaling quantum transport calculations on graphics processing units and applications on electron transport in graphene, Comput. Phys. Commun. 185, 28 (2014). https://doi.org/10.1016/j.cpc.2013.08.009
This code was first published along with the following paper:
- [3] Z. Fan, V. Vierimaa, and Ari Harju, GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units, Comput. Phys. Commun. 230, 113 (2018). https://doi.org/10.1016/j.cpc.2018.04.013
There is a comprehensive review article discussing the linear scaling quantum transport methods:
- [4] Zheyong Fan, Jose Hugo Garcia, Aron W. Cummings, Jose-Eduardo Barrios, Michel Panhans, Ari Harju, Frank Ortmann, and Stephan Roche, Linear Scaling Quantum Transport Methodologies, Physics Reports 903, 1 (2021). https://doi.org/10.1016/j.physrep.2020.12.001