| 基于Fork/Join多核并行框架的梯级水库群优化调度 |
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| 引用本文： | 王森,马志鹏,李善综,王凌河,熊静.基于Fork/Join多核并行框架的梯级水库群优化调度[J].水利水电科技进展,2017,37(2):48-54. |
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| 作者姓名： | 王森 马志鹏 李善综 王凌河 熊静 |
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| 作者单位： | 珠江水利科学研究院, 广东 广州 510611; 水利部珠江河口动力学及伴生过程调控重点实验室, 广东 广州 510611,珠江水利科学研究院, 广东 广州 510611; 水利部珠江河口动力学及伴生过程调控重点实验室, 广东 广州 510611,水利部珠江水利委员会技术咨询中心, 广东 广州 510611,珠江水利科学研究院, 广东 广州 510611; 水利部珠江河口动力学及伴生过程调控重点实验室, 广东 广州 510611,珠江水利科学研究院, 广东 广州 510611; 水利部珠江河口动力学及伴生过程调控重点实验室, 广东 广州 510611 |
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| 基金项目： | 水利部公益性行业科研专项(201401013,201501010) |
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| 摘 要： | 为了满足大规模梯级水库群优化调度精细化管理需求,解决决策计算耗时长及求解效率低等困难,提出了基于Fork/Join多核并行框架的梯级水库群优化调度并行求解方法,并以离散微分动态规划方法并行化为例,给出了梯级水库群优化调度方法在Fork/Join框架下的并行化实现方式。红水河大规模梯级水库群长期发电优化调度测试结果表明,并行计算能够充分发挥多核处理器的加速性能,有效缩短计算耗时,提高求解效率;选择合理的Fork/Join框架规模控制阈值是充分发挥并行优势的关键因素。
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| 关 键 词： | 梯级水库群 优化调度 Fork/Join并行框架 多核处理器 并行计算 |
| 收稿时间： | 2016/2/12 0:00:00 |
Optimal operation of cascaded reservoirs based on Fork/Join multi-core parallel framework |
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| WANG Sen,MA Zhipeng,LI Shanzong,WANG Linghe and XIONG Jing.Optimal operation of cascaded reservoirs based on Fork/Join multi-core parallel framework[J].Advances in Science and Technology of Water Resources,2017,37(2):48-54. |
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| Authors: | WANG Sen MA Zhipeng LI Shanzong WANG Linghe and XIONG Jing |
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| Affiliation: | Pearl River Hydraulic Research Institute, Guangzhou 510611, China; Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou 510611, China,Pearl River Hydraulic Research Institute, Guangzhou 510611, China; Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou 510611, China,Technical Advisory Center of Pearl River Resources Commission, Ministry of Water Resources, Guangzhou 510611, China,Pearl River Hydraulic Research Institute, Guangzhou 510611, China; Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou 510611, China and Pearl River Hydraulic Research Institute, Guangzhou 510611, China; Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou 510611, China |
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| Abstract: | In order to meet the refined management demand of optimal operation of large-scale cascaded reservoirs and solve the problems of long running time and low computational efficiency, a parallel method based on the Fork/Join multi-core parallel framework is proposed for optimal operation of large-scale cascaded reservoirs. A parallel discrete differential dynamic programming(PDDDP)was designed to describe the parallelization scheme for optimal operation of cascaded reservoirs based on the Fork/Join framework. The long-term power generation optimal operation of large-scale cascaded reservoirs on the Hongshui River was used as a case study. The testing results show that the parallel method can make full use of the acceleration performance of the multi-core processor, significantly reducing the computation time, and improving the computational efficiency. Moreover, the choice of the reasonable scale control threshold of the Fork/Join framework is critical to taking full advantage of parallel computation. |
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| Keywords: | cascaded reservoirs optimal operation Fork/Join parallel frame multi-core processor parallel computing |
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