gpuGEM: GPU-based linear optimisation of genome-scale metabolic models
Abstract
Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated optimisation. We present gpuGEM, an open-source Python library for GPU-based optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across ten models spanning four orders of magnitude, gpuGEM achieved 1.1x to 94x speed-ups over the faster HiGHS method (dual simplex or interior point), and outperformed Gurobi on three models (1.6x to 2.5x). In candidate-gene prioritisation, gpuGEM reduced end-to-end query time from 41 s to 21 s. Availability and Implementation: gpuGEM is released under the MIT licence at https://github.com/Digital-Metabolic-Twin-Centre/gpuGEM and on PyPI (pip install gpugem); release v0.1.3 is archived at https://zenodo.org/records/23422813. It requires an NVIDIA GPU of compute capability 7.0 or newer (Volta onward), Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. Contact: [email protected] Supplementary information: Supplementary data are available as an ancillary file on arXiv.
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BibTeX
@misc{zare2026gpugem,
title = {{gpuGEM: GPU-based linear optimisation of genome-scale metabolic models}},
author = {Farid Zare and Timothy Hulshof and Ines Thiele and Ronan M. T. Fleming},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24106},
eprint = {2610.24106},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24106},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - gpuGEM: GPU-based linear optimisation of genome-scale metabolic models
AU - Farid Zare
AU - Timothy Hulshof
AU - Ines Thiele
AU - Ronan M. T. Fleming
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.24106
UR - https://arxiv.org/abs/2610.24106
AB - Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated optimisation. We present gpuGEM, an open-source Python library for GPU-based optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across ten models spanning four orders of magnitude, gpuGEM achieved 1.1x to 94x speed-ups over the faster HiGHS method (dual simplex or interior point), and outperformed Gurobi on three models (1.6x to 2.5x). In candidate-gene prioritisation, gpuGEM reduced end-to-end query time from 41 s to 21 s. Availability and Implementation: gpuGEM is released under the MIT licence at https://github.com/Digital-Metabolic-Twin-Centre/gpuGEM and on PyPI (pip install gpugem); release v0.1.3 is archived at https://zenodo.org/records/23422813. It requires an NVIDIA GPU of compute capability 7.0 or newer (Volta onward), Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. Contact: [email protected] Supplementary information: Supplementary data are available as an ancillary file on arXiv.
ER -