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Linear Programming
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Scaling Decision Optimization to 100 Million Variables and Beyond with mPDLP in NVIDIA cuOpt
Supply chain and energy grid planning problems now exceed the memory and time limits of single-GPU solvers. NVIDIA cuOpt introduces mPDLP, a multi-GPU primal-dual hybrid gradient solver that distributes linear programming problems across NVLink-connected GPUs. The algorithm’s hot loop is a sparse matrix-vector multiplication, a memory-bound operation that benefits from the increased bandwidth of multiple GPUs. Min-cut partitioning reduces communication overhead by keeping tightly connected dependencies on the same GPU, enabling up to 6x lower peak memory per GPU. On the benchmark problem zib03 with over 104 million nonzeros, mPDLP achieved a solve rate nearly 10x faster than the previous year, and partners Kinaxis and PSR demonstrated 3.3x and 5x speedups on real-world models.
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