Efficient linear-scaling quantum transport calculations on graphics processing units and applications on electron transport in graphene
Zheyong Fan, Andreas Uppstu, Topi Siro, Ari Harju

TL;DR
This paper develops and optimizes a GPU-based linear-scaling quantum transport simulation using the Kubo-Greenwood approach, demonstrating significant speedups and validating methods on graphene electron transport, including ballistic and diffusive regimes.
Contribution
It introduces a GPU-accelerated implementation of the linear-scaling Kubo-Greenwood quantum transport method with optimized algorithms for graphene applications.
Findings
GPU implementation achieves up to 16x speedup over CPU.
The kernel polynomial method is more efficient than Fourier transform for delta function approximation.
The Einstein formula accurately reproduces quantized conductance in ballistic graphene nanoribbons.
Abstract
We implement, optimize, and validate the linear-scaling Kubo-Greenwood quantum transport simulation on graphics processing units by examining resonant scattering in graphene. We consider two practical representations of the Kubo-Greenwood formula: a Green-Kubo formula based on the velocity auto-correlation and an Einstein formula based on the mean square displacement. The code is fully implemented on graphics processing units with a speedup factor of up to 16 (using double-precision) relative to our CPU implementation. We compare the kernel polynomial method and the Fourier transform method for the approximation of the Dirac delta function and conclude that the former is more efficient. In the ballistic regime, the Einstein formula can produce the correct quantized conductance of one-dimensional graphene nanoribbons except for an overshoot near the band edges. In the diffusive regime,…
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