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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

Japanese

概要:ゲノム規模の代謝データモデルは、繰り返し最適化のワークフローでCPUの線形プログラミングソリュータにますます挑戦します。我々は、NVIDIA cuOptを使用してGPUベースのGEM最適化のためのオープンソースのPythonライブラリであるgpuGEMを、HiGHSとGurobiに対応したサイズ適応設定で、ますます挑戦します。問題サイズの大きさの4つの順序をカバーする10モデルのベンチマークスイートを通じて、gpuGEMは、各モデルのHiGHSダブルシンプルおよびインテリアポイントメソッドの速さと比較して1.1x〜94xのスピードアップを達成しました。gpuGEMはまた、10

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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