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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

German

Zusammenfassung: Genome-Skala-Metabolische Datenmodelle werden zunehmend mit CPU-Linearprogrammierungslösern in wiederholten Optimierungs-Workflows herausgefordert. Wir präsentieren gpuGEM, eine Open-Source-Python-Bibliothek für GPU-basierte GEM-Optimierung mit NVIDIA cuOpt, mit Größen-Adaptive-Einstellungen, die gegen HiGHS und Gurobi validiert wurden. Über eine Zehn-Modell-Benchmark-Suite, die vier Größenordnungen in Problemgröße umfasst, erzielte gpuGEM Geschwindigkeiten von 1.1x bis 94x im Vergleich zu den schnelleren der HiGHS Dual-Simplex- und Inneren-Punkt-Methoden für jedes Modell. gpuGEM übertrifft

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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