cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929201895964672 |
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| author | Lu, Haihao Yang, Jinwen Hu, Haodong Huangfu, Qi Liu, Jinsong Liu, Tianhao Ye, Yinyu Zhang, Chuwen Ge, Dongdong |
| author_facet | Lu, Haihao Yang, Jinwen Hu, Haodong Huangfu, Qi Liu, Jinsong Liu, Tianhao Ye, Yinyu Zhang, Chuwen Ge, Dongdong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14832 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language Lu, Haihao Yang, Jinwen Hu, Haodong Huangfu, Qi Liu, Jinsong Liu, Tianhao Ye, Yinyu Zhang, Chuwen Ge, Dongdong Optimization and Control 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. |
| title | cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2312.14832 |