gpuqt

KO | EN

gitlite — search

#typescript #ai-agents #ai #dsh-plugin #deepseek-harness #open-source #claude-code #codex #cli #developer-tools #react #windows

★ 28 Open GitHub ↗

A CUDA-implementation of linear-scaling quantum transport methods

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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 the `make` 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 the `cl.exe` compiler from MSVC and a `make.exe` program which can be downloaded [here](http://www.equation.com/servlet/equation.cmd?fa=make)) and Linux systems.

## Installing

* Go to `src` and 
    * type `make -f makefile.cpu` to build the CPU version. This will produce an executable called `lsqt_cpu` in the `src` folder.
    * type `make -f makefile.gpu` to build the GPU version. This will produce an executable called `lsqt_gpu` in the `src` folder.

## 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 in `examples/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

gpuqt

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README