GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Zheyong Fan

doi:10.1016/j.cpc.2018.04.013

2018-09-01

Zheyong Fan *

Ville Vierimaa

Ari Harju

DOI：10.1016/j.cpc.2018.04.013

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摘要

En 中文

We present GPUQT, a quantum transport code fully implemented on graphics processing units. Using this code, one can obtain intrinsic electronic transport properties of large systems described by a real-space tight-binding Hamiltonian together with one or more types of disorder. The DC Kubo conductivity is represented as a time integral of the velocity auto-correlation or a time derivative of the mean square displacement. Linear scaling (with respect to the total number of orbitals in the system) computation time and memory usage are achieved by using various numerical techniques, including sparse matrix-vector multiplication, random phase approximation of trace, Chebyshev expansion of quantum evolution operator, and kernel polynomial method for quantum resolution operator. We describe the inputs and outputs of GPUQT and give a few examples to demonstrate its usage, paying attention to the interpretations of the results. Program summary Program Title: GPUQT Program Files doi: http://dx.doi.org/10.17632/xbf5kbkzx7.1 Licensing provisions: GPLv3 Programming language: CUDA Nature of problem: Obtain intrinsic electronic transport properties of large systems described by real-space tight-binding Hamiltonians. Solution method: The DC conductivity is represented as a time integral of the velocity auto-correlation (VAC) or a time derivative of the mean square displacement (MSD). The calculations achieve linear scaling (with respect to the number of orbitals in the system) computation time and memory usage by using various numerical techniques, including sparse matrix-vector multiplication, random phase approximation of trace, Chebyshev expansion of quantum evolution operator, and kernel polynomial method for quantum resolution operator. Restrictions: The number of orbitals is restricted to about 20 million due to the limited amount of device memory in current GPUs. (C) 2018 Elsevier B.V. All rights reserved.

Keyword:

Quantum transport

Linear-scaling

GPU acceleration