Codebase release 1.0 for TorchGPE

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

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Publication Details

Journal
SciPost Physics Codebases
Published
2024-11-06
DOI
https://doi.org/10.21468/scipostphyscodeb.38-r1.0
Citations
2
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.70

Funders

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article

Codebase release 1.0 for TorchGPE

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2 citations
SciPost Physics Codebases
Distributed and Parallel Computing Systems
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Codebase release 1.0 for TorchGPE

Davide Dreon, Tobias Donner, Alexander Baumgärtner, Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak, Fabian Finger
article en
2 citations

Abstract

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

SciPost Physics Codebases
ETH Zurich (CH), École Polytechnique Fédérale de Lausanne (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Staatssekretariat für Bildung, Forschung und Innovation
Openalex Percentile: Top 27%
Distributed and Parallel Computing Systems
0.70
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