NEWS · COMPANIES · #1678
NVIDIA cuOpt adds mPDLP multi‑GPU LP solver that scales to 100M+ variables
NVIDIA released mPDLP, a Multi‑GPU Primal‑Dual hybrid gradient solver in cuOpt that shards large linear programs across NVLink‑connected GPUs. mPDLP cuts per‑GPU peak memory (up to 6× vs single‑GPU PDLP), supports problems up to 2.1B nonzeros, and in DGX B200 benchmarks shows PDLP‑step speedups up to 11.4× and typical overall gains of 1.2×–2.5× vs prior D‑PDLP; partner cases reported ~3.3× (Kinaxis, 135M variables) and >5× (PSR, 185M variables). A tutorial and source code are available on GitHub.
KEY POINTS
- NVIDIA released mPDLP, a Multi‑GPU Primal‑Dual hybrid gradient solver in cuOpt that shards large linear programs across NVLink‑connected GPUs.
- mPDLP cuts per‑GPU peak memory (up to 6× vs single‑GPU PDLP), supports problems up to 2.1B nonzeros, and in DGX B200 benchmarks shows PDLP‑step speedups up to 11.4× and typical overall gains of 1.2×–2.5× vs prior D‑PDLP; partner cases reported ~3.3× (Kinaxis, 135M variables) and >5× (PSR, 185M variables).
- A tutorial and source code are available on GitHub.
WHY IT MATTERS
mPDLP removes single‑GPU memory and time bottlenecks, letting planners and researchers solve much larger LPs (supply‑chain, energy models) within practical time windows.