NVIDIA cuOpt Open Source: GPU-Accelerated Decision Optimization for Faster LP, MIP, and VRP Solving

Businesses face numerous computational challenges when optimizing daily decisions, such as what to produce, where to ship goods, and how to allocate resources. Traditional methods like linear programming (LP), mixed-integer programming (MIP), and vehicle routing problems (VRP) offer structured solutions, but the main bottleneck lies in solving these problems quickly at scale. NVIDIA has introduced cuOpt, an open-source tool available under the Apache 2.0 license, designed to accelerate decision optimization using GPU technology. cuOpt delivers significant performance improvements for LP, MIP, and VRP tasks, making it easier for businesses to optimize their workflows locally or in the cloud. Key Features and Benefits Near-Zero Changes in Modeling Languages One of cuOpt's standout features is its seamless integration with existing optimization models. Developers can incorporate cuOpt into models built with popular tools like PuLP and AMPL with minimal changes. This means that switching solvers doesn’t require a complete rewrite, ensuring a smooth transition. Real-World Use Case: Coffee Logistics Consider a global coffee chain that sources, roasts, packages, and ships thousands of bags of beans annually. Each step in the supply chain is constrained by facility capacities and fluctuating demand. cuOpt helps solve these complex problems by providing rapid reoptimization capabilities. For instance, if a roastery becomes unavailable, cuOpt can quickly reroute orders and reassign suppliers, maintaining efficiency without significant delays. Delivery logistics add another layer of complexity, requiring drivers to be routed across various orders and time windows while adhering to labor rules and shift limitations. cuOpt excels in handling these dynamic scenarios, offering up to 20x speedups compared to traditional methods, as demonstrated by the collaboration between Artelys and FICO in energy modeling. Quick Start: Solve Your First Problem in Minutes NVIDIA provides several entry points for users to start with cuOpt: cuOpt Server: Ideal for LP, MIP, and VRP problems, cuOpt Server operates through a REST API. Users can set up a server instance that supports various optimization tasks, making it easy to integrate into existing workflows. Python API: Best suited for VRP tasks, cuOpt’s native Python API allows for programmatic control and seamless integration into Python-based projects. Command-line Interface (CLI): Perfect for benchmarking LP and MIP models, the CLI supports MPS-format files. For example, an LP problem with 69,000 constraints and 17,000 variables can be solved in under 0.3 seconds on an NVIDIA H100 Tensor Core GPU. Cloud Deployment: For those without local GPUs, cuOpt can be run via Google Colab or NVIDIA Launchable. Google Colab offers free, limited GPU access, while Launchable provides a persistent environment with full GPU capabilities. Table 1 below summarizes the options: Cloud Deployment Options for Running cuOpt - Google Colab: No setup required, manual preloaded configuration, ideal for demos and quick tests. - Launchable: One-click launch, automatic preloaded configuration, suitable for full development workflows. Implementation Examples Integrating cuOpt with AMPL and PuLP AMPL Example: Switch to cuOpt by changing the solver command. For MIP problems, declare variables as integers. This simple change can significantly enhance performance. PuLP Example: Similar to AMPL, switching to cuOpt involves changing the solver to cuOptSolver. For MIP problems, ensure variables are declared as integers. Solving VRP with cuOpt cuOpt’s Python client handles VRP problems with structured JSON inputs. The workflow involves defining the problem, specifying the data, and calling the cuOpt solver. The tool returns optimized routes, cost, and task-level assignments, making it ideal for logistics and dispatch systems. Community and Ecosystem NVIDIA has partnered with COIN-OR, a leading hub for open-source operations research tools, to distribute cuOpt through the coin-or/cuopt GitHub repository. This partnership strengthens the ecosystem, making cuOpt more accessible, extendable, and compatible with other open-source solvers. Users can join the community, benefit from full control and flexibility, and help shape the future of real-time, intelligent decision optimization. Industry Evaluation Industry experts have praised cuOpt for its significant speedups and ease of integration. The ability to handle large-scale, complex optimization problems without extensive code changes makes it highly valuable for businesses looking to streamline their operations and improve decision-making processes. NVIDIA's commitment to open-source development and collaboration with COIN-OR further underscores the tool's potential to become a cornerstone in the optimization toolkit of developers and businesses alike. Company Profile NVIDIA, a pioneer in GPU technology, continues to innovate with tools like cuOpt. Known for its expertise in parallel computing, NVIDIA has a strong track record of developing high-performance software solutions that leverage its hardware. cuOpt is another example of this synergy, demonstrating how GPU acceleration can transform traditional computational bottlenecks into efficient, real-time optimizations.