Accelerate Large Linear Programming Problems with NVIDIA cuOpt
The evolution of linear programming (LP) solvers has been marked by significant milestones over the past century, from Simplex to the interior point method (IPM). The introduction of primal-dual linear programming (PDLP) has brought another significant advancement.
NVIDIA cuOpt has now implemented PDLP with GPU acceleration. Using cutting-edge algorithms, NVIDIA hardware, dedicated CUDA features, and NVIDIA GPU libraries, the cuOpt LP solver achieves over 5,000x faster performance compared to CPU-based solvers.
This post examines the key components of LP solver algorithms, GPU acceleration in LP, and cuOpt performance on Mittelmann’s benchmark and Min Cost Flow problem instances.
Consider this scenario: A farmer must decide which vegetables to grow and in what quantities to maximize profit, given limitations on land, seeds, and fertilizer. The goal is to determine the optimal revenue while respecting all constraints, as quickly as possible.
NVIDIA developed an LLM agent example that helps model the problem and solve it using an LP solver. LP is an essential tool for optimization and has applications in resource allocation, production planning, supply chain, and, as a backbone for mixed-integer programming (MIP) solvers. Solving mathematical problems with millions of variables and constraints in seconds is challenging, if not impossible, in some cases.
