Newest papers

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

Chinese

摘要:基因组规模的代谢数据模型在重复优化工作流中越来越多地挑战CPU线性编程解决方案。我们介绍了gpuGEM,一个开源的Python库用于GPU基于GEM的优化,使用NVIDIA cuOpt,大小适应性设置对HiGHS和Gurobi进行了验证。在一个十个模型的基准套件中,在问题大小的四个大小顺序中,gpuGEM实现了1.1x至94x的速度提升,相对于每一个模型的HiGHS双简单和内部点方法的速度更快。gpuGEM还优化了Gurobi在10个在线模型中的三种,速度提升为1.6x至2.5x。在一个候选基因优先级工作流中,gpuG

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange