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Main Authors:	Hu, Xiyan, Parker, Titus, Phillips, Connor, Yu, Yifa
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control
Online Access:	https://arxiv.org/abs/2508.16806
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author	Hu, Xiyan Parker, Titus Phillips, Connor Yu, Yifa
author_facet	Hu, Xiyan Parker, Titus Phillips, Connor Yu, Yifa
contents	Linear Programming (LP) is a foundational optimization technique with widespread applications in finance, energy trading, and supply chain logistics. However, traditional Central Processing Unit (CPU)-based LP solvers often struggle to meet the latency and scalability demands of dynamic, high-dimensional industrial environments, creating a significant computational challenge. This project addresses these limitations by accelerating linear programming on AMD Graphics Processing Units (GPUs), leveraging the ROCm open-source platform and PyTorch. The core of this work is the development of a robust, high-performance, open-source implementation of the Primal-Dual Hybrid Gradient (PDHG) algorithm, engineered specifically for general LP problems on AMD hardware. Performance is evaluated against standard LP test sets and established CPU-based solvers, with a particular focus on challenging real- world instances including the Security-Constrained Economic Dispatch (SCED) to guide hyperparameter tuning. Our results show a significant improvement, with up to a 36x speedup on GPU over CPU for large-scale problems, highlighting the advantages of GPU acceleration in solving complex optimization tasks.
format	Preprint
id	arxiv_https___arxiv_org_abs_2508_16806
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Accelerating a Linear Programming Algorithm on AMD GPUs Hu, Xiyan Parker, Titus Phillips, Connor Yu, Yifa Optimization and Control Linear Programming (LP) is a foundational optimization technique with widespread applications in finance, energy trading, and supply chain logistics. However, traditional Central Processing Unit (CPU)-based LP solvers often struggle to meet the latency and scalability demands of dynamic, high-dimensional industrial environments, creating a significant computational challenge. This project addresses these limitations by accelerating linear programming on AMD Graphics Processing Units (GPUs), leveraging the ROCm open-source platform and PyTorch. The core of this work is the development of a robust, high-performance, open-source implementation of the Primal-Dual Hybrid Gradient (PDHG) algorithm, engineered specifically for general LP problems on AMD hardware. Performance is evaluated against standard LP test sets and established CPU-based solvers, with a particular focus on challenging real- world instances including the Security-Constrained Economic Dispatch (SCED) to guide hyperparameter tuning. Our results show a significant improvement, with up to a 36x speedup on GPU over CPU for large-scale problems, highlighting the advantages of GPU acceleration in solving complex optimization tasks.
title	Accelerating a Linear Programming Algorithm on AMD GPUs
topic	Optimization and Control
url	https://arxiv.org/abs/2508.16806

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