GPU-Accelerated Decision Optimization
Abstract
For decades, algorithms for solving decision optimization problems have been designed and implemented for CPUs. Recent advances in GPU hardware, driven by machine learning and artificial intelligence (AI), together with the development of GPU-accelerated scientific-computing kernels, have made GPUs a practical platform for solving optimization problems. This shift is most visible in linear programming, where first-order methods exploit GPU parallelism and high-bandwidth memory to solve problems with millions of variables and constraints. GPU algorithms for mixed-integer programming are still emerging, whereas GPU-based vehicle-routing solvers are more mature and already support large-scale routing applications. At the same time, generative AI and agentic AI are changing how optimization models are formulated, solved, and embedded in decision workflows. This tutorial introduces GPU-accelerated decision optimization for researchers and practitioners familiar with CPU-based modeling and solvers. We review computational patterns that make optimization algorithms amenable to GPU acceleration, summarize the growing ecosystem of open-source and commercial GPU solvers, and provide benchmarks that quantify current performance. We also present cuOpt, NVIDIA’s open-source library for GPU-accelerated decision optimization. Our goal is to help the optimization community understand where GPUs are useful today, where important limitations remain, and where future research and development are needed.
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