Part 1: How GPUs Are Impacting the Future of Optimization, And Where it Matters Most
Published on September 29, 2025
Note: This article is the first in a four-part series on GPU acceleration for optimization. We’ll begin here at a high level, with a focus on why GPUs matter for business decision-making and where they can make the most impact. Later posts will go progressively deeper into the math, algorithms, and solver design. If you’re a business leader, this first post should be accessible; if you’re an optimization expert, stick with us—the technical depth is coming.
When you think of GPUs (Graphics Processing Units), you might picture gaming, AI model training, or cryptocurrency mining. Yet in NVIDIA CEO Jensen Huang’s 2025 GTC keynote in Paris, he didn’t mention any of these. Instead, within the first two minutes, he highlighted mathematical optimization.
Over the past couple of years, NVIDIA has increasingly focused on optimization, culminating in the recent open-source launch of cuOpt, their GPU-accelerated optimization solver. But why the focus on optimization? After all, operations research and management science (OR/MS) methods – often bundled into the term “optimization” – have been around for decades.
The reason it’s still a hot topic is that the world is still filled with hard optimization problems. So hard, in fact, that even after all these decades, many real-world optimization problems remain too complex or computationally expensive to solve efficiently. But that’s about to change.
Recent advances across multiple areas like high performance compute, software algorithms, massive parallelism and even some of the newer AI and machine learning methods are now converging to make large-scale, high-impact optimization practical. Together, these developments are bringing optimization within reach for a broader range of industries and problems in supply chain, production, logistics, and beyond – at scales and speeds that were previously out of reach.
If you attend the annual INFORMS conference—the premier gathering for operations research (O.R.) and analytics professionals—or follow their prestigious Edelman Awards, which recognize outstanding applications of O.R. in business and public policy, you’ll hear plenty of success stories about O.R.-savvy companies achieving results with custom APS (Advanced Planning and Scheduling) solutions. What’s rarely mentioned, however, is how difficult and costly it can be to adapt even the best-known optimization methods and models to a given company’s specific business needs. For instance, adding a seemingly simple business constraint, like a limit on how certain products can be grouped in a shipment, can increase the computational demand of creating a logistics plan by several orders of magnitude, making the problem dramatically harder to solve in practice.
Because of challenges like this, even the best operations research teams often end up simplifying their models to produce results that may be acceptable but not necessarily great. We discussed this in our blog article entitled Speed Isn’t the Point: What Faster Solvers Really Unlock.
In this post, we’ll explain why GPUs change the game for optimization, highlight use cases where GPU acceleration offers clear advantages, and show why innovation leaders should rethink optimization as a powerful, modern technology that is becoming far more accessible.
CPU vs GPU: Different Engines for Different Jobs
To understand the advantages GPUs bring to optimization, it helps to first look at how CPUs and GPUs handle parallelization differently. A restaurant or “food factory” analogy works well here
Imagine you open a restaurant and do all the cooking yourself. You chop the vegetables, sauté them, add the chicken and some oil, stir, and finally plate the dish before starting over. That’s like a single-threaded CPU: one worker doing one task at a time in sequence.
As business picks up, you hire more kitchen staff. One person chops parsley for one order, another plates an earlier order, while you stay at the stove stirring 3 different pots for several orders, one of which will consume the parsley. These can all happen in parallel. This is closer to a multi-core CPU—each worker can do a different task at the same time. But there’s a limit. Add too many cooks and the kitchen gets crowded, workers start bumping into each other, and you’ll need to open a second kitchen, with all the cost and administrative overhead that involves.
GPUs are different. Instead of a few workers doing many different jobs, imagine an army of small kitchen robots, each assigned the exact same task. Picture 500 mini chop-bots (mini, so they’ll never get in each other’s way), all chopping in perfect unison – some onions, some peppers, some chicken – before the parallel stir-bot gets engaged, some of its spoons stirring a pot with onions, some stirring onions, peppers and chicken together in a pan – before engaging the parallel whisk-bot. You get the idea, right?
On its own, that sounds restrictive. Why would you ever want everyone doing the same thing? The answer lies in scale. If you have, say, a million meals to serve and therefore mountains of ingredients to chop, the GPU’s approach clears the backlog at incredible speed and without the cost/hassle of having to open more restaurant kitchens. However, the trick is in intelligently organizing the work, which first requires breaking the optimization problems into patterns that benefit from this massive parallelism.
The clever decomposition and organization part is the key. Think back to our “food factory” analogy. With a single GPU you can parallelize tasks so that everyone does the same task in lockstep, but across a range of ingredients. First, all the cooks whisk different bowls of ingredients – one whisks an aioli, another is whipping cream or a salad dressing. Then, all chop, but they can be chopping different ingredients in parallel. Then, all stir different pots/pans on the stove. Each phase is done at a massive scale before moving to the next. This is useful when an algorithm naturally progresses through synchronized stages, with each stage requiring many identical operations.
The diagram above shows what the whole process could look like over time, as the vertical bar moves left to right.
Now coming back to the world of solving optimization problems, we’re not cooking meals but solving puzzles with millions of moving parts, dependencies, and constraints. Or, if you will, searching through a vast and lumpy search space to find the best plan/schedule/route, or at least one that’s super close to best – quickly. That’s where the real complexity comes in and will be the topic of the next post in our series.
Where This Matters in the Real World
The value of GPU acceleration is clearest in situations where the planning problem is both complex and time-sensitive. A schedule that works at 8:00 AM may be obsolete by 10:00 because a machine broke down, a rush order arrived, or Sarah called in sick. Traditional approaches try to cope with this through partial re-optimization—locking most of a plan in place while re-solving only a subset. This makes the problem faster to solve with minimal disruption to the overall plan, but the workarounds are often brittle, based on oversimplified assumptions, and prone to failure. In many cases, planners end up stepping outside the system to manually overwrite the plan, which usually produces less efficient results.
GPU-native solvers change the ROI calculation. By splitting problems into many parts and solving them simultaneously, it becomes possible to re-optimize continually. This means that decisions can be made based on the most up-to-date information—like the new rush order sales wants you to squeeze in.
This agility matters not just for operational scheduling but also for so-called NP-hard problems, like vehicle routing, crew scheduling, network design, or blending and portfolio optimization – in which the search space grows so quickly that even when you are happy to wait overnight for a good solution, traditional solvers still struggle. Some of this is already possible today, and much more will be achievable in the months and years ahead.
So why isn’t everyone using GPU-native solvers?
Coming Next: Why You Can’t Just Port a Solver to Run on GPUs
In the next section, we’ll explain why you can’t simply run a legacy solver on a GPU, what “GPU-native” really means, and how SimpleRose is working toward combining NVIDIA cuOpt with our own optimization layers. Our goal is to deliver fast, practical, high-quality solutions that can keep up with real-world, time-sensitive operations.
