  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.

Optimization and Control