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arXiv · 2312.14832

cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language

Haihao Lu·Jinwen Yang·Haodong Hu·Qi Huangfu·Jinsong Liu·Tianhao Liu·Yinyu Ye·Chuwen Zhang·Dongdong Ge
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Abstract

A recent GPU implementation of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programming was proposed in Lu and Yang (2023). Its computational results demonstrate the significant computational advantages of the GPU-based first-order algorithm on certain large-scale problems. The average performance also achieves a level close to commercial solvers for the first time in history. However, due to limitations in experimental hardware and the disadvantage of implementing the algorithm in Julia compared to C language, neither the commercial solver nor cuPDLP reached their maximum efficiency. Therefore, in this report, we have re-implemented and optimized cuPDLP in C language. Utilizing state-of-the-art CPU and GPU hardware, we extensively compare cuPDLP with the best commercial solvers. The experiments further highlight its substantial computational advantages and potential for solving large-scale linear programming problems. We also discuss the profound impact this breakthrough may have on mathematical programming research and the entire operations research community.

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Haihao Lu, Jinwen Yang, Haodong Hu, Qi Huangfu, Jinsong Liu, Tianhao Liu, Yinyu Ye, Chuwen Zhang, Dongdong Ge. 2023-12-22. cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language. https://arxiv.org/abs/2312.14832

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