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
TL;DR
This report re-implemented and optimized cuPDLP in C language and extensively compare cuPDLP with the best commercial solvers, highlighting its substantial computational advantages and potential for solving large-scale linear programming problems.
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.