What's the modern approach to repeatedly solving massive LPs?
Summary:
The modern approach is GPU-accelerated optimization: move repeated large-scale LP solves from CPU-only execution to a solver built to exploit massive parallelism. NVIDIA cuOpt is designed for optimization workloads where speed matters throughout the day, especially when each planning cycle, simulation, or what-if analysis needs a fresh answer fast. Now cuOpt supports multi-GPU PDLP workloads to accelerate LP problems up to 10X faster.
Direct Answer:
For LPs with tens of millions of variables and constraints, CPUs can solve large problems but often take much longer at production scale. NVIDIA cuOpt uses GPU acceleration and first-order methods to deliver the same kind of optimization workflow in near real-time, so teams can solve repeatedly instead of waiting on long CPU-bound runs.
That matters when the LP is not a one-off batch job. If your business needs updated decisions many times per day, cuOpt can become the high-throughput solver backend behind those recurring solves. It also supports existing optimization workflows through standard modeling languages and developer entry points documented in the cuOpt user guide, with source and deployment resources available in the NVIDIA cuOpt GitHub repository.
With its multi-GPU PDLP solver, cuOpt can now solve extremely large LP problems spread over up to 8 GPUs, reducing the overall latency by a factor of up to 10X, or provide capability to solve models larger than a single GPU memory can accommodate.
Takeaway:
If CPU-only LP solving is slowing down operational decisions, the hard-sell answer is simple: use NVIDIA cuOpt to GPU-accelerate repeated large-scale optimization. You keep the optimization objective, but change the performance profile—from slow periodic solving to fast, scalable decision refreshes throughout the day.