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gpuGEM: GPU-based linear optimisation of genome-scale metabolic models

Farid Zare, Timothy Hulshof, Ines Thiele, Ronan M. T. Fleming · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24106 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Resumen: Los modelos de datos metabólicos a escala genómica están desafiando cada vez más a los solucionadores de programación lineal de la CPU en flujos de trabajo de optimización repetida. Presentamos gpuGEM, una biblioteca de Python de código abierto para la optimización de GEM basada en GPU utilizando NVIDIA cuOpt, con configuraciones adaptativas de tamaño validadas contra HiGHS y Gurobi. A través de una suite de benchmark de diez modelos que abarca cuatro órdenes de magnitud en tamaño de problema, gpuGEM logró velocidades de 1.1x a 94x en relación con el más rápido de los métodos HiGHS dual-simplex e interior-point para cada modelo. gpuGEM también superó a Gurobi en tres de los diez modelos en línea, con velocidades de 1.6x a 2.5x. En un flujo de trabajo

Machine translation (Meta M2M100); the English original follows.

Abstract

Summary: Genome-scale metabolic models increasingly challenge CPU linear-programming solvers in repeated-optimisation workflows. We present gpuGEM, an open-source Python library for GPU-based GEM optimisation using NVIDIA cuOpt, with size-adaptive settings validated against HiGHS and Gurobi. Across a ten-model benchmark suite spanning four orders of magnitude in problem size, gpuGEM achieved speed-ups of 1.1x to 94x relative to the faster of the HiGHS dual-simplex and interior-point methods for each model. gpuGEM also outperformed Gurobi on three of ten models, with speed-ups of 1.6x to 2.5x. In a candidate-gene prioritisation workflow, 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). An archived release(v0.1.2) is available at https://zenodo.org/records/23422813. gpuGEM requires an NVIDIA GPU, Python 3.11 or later, CUDA 12 and cuOpt 26.6 or later. cuOpt requires a GPU with Volta architecture or newer (compute capability 7.0 or newer). Contact: ronan.mt.fleming@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

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Topics: Python (programming language), Suite, Computer science, CUDA, Benchmark (surveying), Parallel computing

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