Efficient GPU parallelization of electronic transport and nonequilibrium dynamics from electron-phonon interactions in the Perturbo code

Abstract The Boltzmann transport equation (BTE) with electron-phonon ( e -ph) interactions computed from first principles is widely used to study electronic transport and nonequilibrium dynamics in materials. Calculating the e -ph collision integral is the most important step in the BTE, but it remains computationally costly, even with current MPI+OpenMP parallelization. This challenge makes it difficult to study materials with large unit cells and to achieve high resolution in momentum space. Here, we show acceleration of BTE calculations of electronic transport and ultrafast dynamics using graphical processing units (GPUs). We implement a novel data structure and algorithm, optimized for GPU hardware and developed using OpenACC, to process scattering channels and efficiently compute the collision integral. This approach significantly reduces the overhead for data referencing, movement, and synchronization. Relative to the efficient CPU implementation in the open-source package PERTURBO (v2.2.0), used as a baseline, this approach achieves a speed-up of 40 times for both transport and nonequilibrium dynamics on GPU hardware, and achieves nearly linear scaling up to 100 GPUs. The novel data structure can be generalized to other electron interactions and scattering processes. We released this GPU implementation in the latest public version (v3.0.0) of PERTURBO. The new MPI+OpenMP+GPU parallelization substantially accelerates electron-phonon coupling and electron dynamics calculations, providing a scalable computational framework and a foundation for future large-scale studies in both conventional and quantum materials.

Authors

Institutions

Publication Details

Journal
npj Computational Materials
Published
2026-05-16
DOI
https://doi.org/10.1038/s41524-026-02135-5
Primary Topic
Machine Learning in Materials Science
Type
preprint
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Efficient GPU parallelization of electronic transport and nonequilibrium dynamics from electron-phonon interactions in the Perturbo code

Marco Bernardi, Ivan Maliyov, Sergei Kliavinek, Shiyu Peng et al.
npj Computational Materials
Machine Learning in Materials Science
preprint

Efficient GPU parallelization of electronic transport and nonequilibrium dynamics from electron-phonon interactions in the Perturbo code

Marco Bernardi, Ivan Maliyov, Sergei Kliavinek, Shiyu Peng, Jia Yao, Donnie Pinkston
preprint en

Abstract

Abstract The Boltzmann transport equation (BTE) with electron-phonon ( e -ph) interactions computed from first principles is widely used to study electronic transport and nonequilibrium dynamics in materials. Calculating the e -ph collision integral is the most important step in the BTE, but it remains computationally costly, even with current MPI+OpenMP parallelization. This challenge makes it difficult to study materials with large unit cells and to achieve high resolution in momentum space. Here, we show acceleration of BTE calculations of electronic transport and ultrafast dynamics using graphical processing units (GPUs). We implement a novel data structure and algorithm, optimized for GPU hardware and developed using OpenACC, to process scattering channels and efficiently compute the collision integral. This approach significantly reduces the overhead for data referencing, movement, and synchronization. Relative to the efficient CPU implementation in the open-source package PERTURBO (v2.2.0), used as a baseline, this approach achieves a speed-up of 40 times for both transport and nonequilibrium dynamics on GPU hardware, and achieves nearly linear scaling up to 100 GPUs. The novel data structure can be generalized to other electron interactions and scattering processes. We released this GPU implementation in the latest public version (v3.0.0) of PERTURBO. The new MPI+OpenMP+GPU parallelization substantially accelerates electron-phonon coupling and electron dynamics calculations, providing a scalable computational framework and a foundation for future large-scale studies in both conventional and quantum materials.

npj Computational Materials
California Institute of Technology (US)
National Science Foundation, U.S. Department of Energy, Office of Science, Basic Energy Sciences
Openalex Percentile: Top 100%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.