CUDA MPC: A GPU-Native Solver for Model Predictive Control

• URL: http://arxiv.org/abs/2608.03051v1
• Date: Tue, 04 Aug 2026 03:02:04 GMT
• Title: CUDA MPC: A GPU-Native Solver for Model Predictive Control
• Abstract summary: Model Predictive Control (MPC) delivers constraint-aware control on systems with fast dynamics, high-dimensional models, or long horizons.<n>This paper introduces MPC, a GPU-native MPC framework that co-designs the optimization algorithm, execution model, and memory architecture for hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model Predictive Control (MPC) delivers constraint-aware control, but its reliance on online optimization limits its use on systems with fast dynamics, high-dimensional models, or long horizons. Existing GPU implementations typically treat the device as a linear-algebra accelerator, leaving the optimization loop dependent on repeated kernel launches and high-latency memory transfers. This paper introduces CUDA MPC, a GPU-native MPC framework that co-designs the optimization algorithm, execution model, and memory architecture for CUDA hardware. CUDA MPC pairs a parallel-in-horizon alternating direction method of multipliers (ADMM) splitting with a fused CUDA kernel that runs the entire iterative solve on the device. Intermediate optimization variables stay in low-latency, on-chip shared memory, and a localized atomic-flag protocol synchronizes only adjacent horizon blocks, minimizing host intervention, kernel-dispatch overhead, and global-memory traffic. Across six nonlinear robotics benchmarks spanning increasing state dimension and constraint density, CUDA MPC sustains real-time rates at horizons one to two orders of magnitude longer than CPU solvers: it solves an optimization-based collision-avoidance parking problem with 100 s of lookahead within a 0.1 s sampling interval, and is the only solver evaluated that achieves both real-time execution and collision-free coordination for a centralized 10-agent swarm, where acados and CasADi return no feasible solution and require 3.5 s and 4.5 s per solve. Against tensor-framework implementations of the same ADMM splitting, the fused kernel is up to $965\times$ faster.

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