ELEQTRONeX: A GPU-Accelerated Exascale Framework for Non-Equilibrium Quantum Transport in Nanomaterials

TL;DR

ELEQTRONeX is a GPU-accelerated framework that enables efficient, large-scale quantum transport simulations in nanomaterials, significantly reducing computation time and allowing complex 3D device modeling.

Contribution

We developed ELEQTRONeX, a novel GPU-based exascale framework that efficiently solves nonequilibrium quantum transport problems in complex nanomaterial devices.

Findings

Achieved orders of magnitude speedup with GPU multithreading.

Demonstrated excellent scaling on up to 512 GPUs.

Validated accuracy with various 3D device configurations.

Abstract

Non-equilibrium electronic quantum transport is crucial for the operation of existing and envisioned electronic, optoelectronic, and spintronic devices. The ultimate goal of encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulation approach has traditionally been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively-parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green's function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve orders of magnitude improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors. We also demonstrate that ELEQTRONeX is suitable for complex device/material geometries where periodic approaches are not feasible, such as modeling of arrays of misaligned carbon nanotubes requiring fully 3D simulations.