Dynamic flux balance analysis (dFBA) and its community, spatial and ensemble variants predict microbial metabolism by solving one linear program (LP) per species and time step, often 10^6 to 10^8 LPs per study. These LPs share their constraint matrix and objective and differ only in their bounds, yet general-purpose solvers treat each one independently, and GPU first-order solvers struggle with the degenerate, badly scaled matrices of genome-scale models. Moreover, metabolic optima are usually faces rather than vertices, so solvers return arbitrary flux vectors and dFBA predictions depend on the solver used. We present manylp, a batched solver for such bounds-parametric LP families that returns certified vertex solutions. Building on the bunching technique of stochastic programming, manylp caches optimal bases and certifies whole batches with one dense matrix product per basis on CPUs or GPUs; LPs outside the cached critical regions are re-solved by a warm-started lexicographic dual simplex, and new bases are propagated across the batch. Each LP is certified optimal and unique under a lexicographic flux rule, or infeasible, in independent double-precision arithmetic. On a genome-scale dFBA workload, manylp certified 47,889 LPs per second on a CPU, 12 to 33 times faster than HiGHS, Gurobi, FICO Xpress, GLPK and OR-Tools (32 processes), and reached 682,000 LPs per second on one GPU for community ensembles, needing 76 simplex solves for 94.6 million LPs. Integrated into the muODE simulator, it accelerated single trajectories 84-fold and 16-member ensembles 635-fold, and made adaptive Runge-Kutta integration affordable, reducing biomass errors 83-fold. Integrating 49 equally optimal flux policies in one batched run showed that alternative optima change predicted acetate 4.2-fold and shift community composition. manylp is open source: https://github.com/cumbof/manylp.

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