Harnessing GPU Acceleration in Large-Scale Process Optimization

Abstract

This paper presents a proof-of-concept workflow for equation-oriented process optimization that runs entirely on a GPU. Process optimization models often incorporate complex interconnected unit operations, dynamics, and uncertainties, resulting in large nonlinear programs that can be computationally demanding for conventional CPU-based solvers. Although emerging GPU-based solvers offer substantial computational benefits, their application to process optimization has been limited by the lack of GPU-compatible process modeling tools. We address this gap by prototyping the GPU-compatible process optimization models using an existing GPU-capable optimization software stack, including ExaModels (algebraic modeling system), MadNLP (optimization solver), and cuDSS (linear solver). ExaModels formulates the process optimization problem in a GPU-compatible way by exposing its repeated algebraic structure, while MadNLP and cuDSS solve the resulting nonlinear program on the GPU. This workflow is demonstrated on a CO2 absorber design problem under feed uncertainty, in which a shared column diameter is minimized subject to equilibrium and hydraulic constraints in all scenarios. For the largest case with 5,000 scenarios and 1.5 million variables, the GPU workflow achieves a speedup of approximately 21\times over a single-threaded CPU baseline using JuMP, Ipopt, and MA57.

Disclosure

“P/Ipopt, CPU-based ExaModels.jl/MadNLP.jl, and DECLARATION OF USE OF AI GPU-based ExaModels.jl/MadNLP.jl. The CPU configura- Generative AI tools were used solely to assist with tions were executed in single-threaded mode to serve as language editing and to improve the clarity, concision, a sequent”

PDF page 5

Classification

Rewriting existing author-written text

Multiplier

4

Verified

2026-08-23T18:57:38.745558Z

Structural counts