流水车间调度问题的GPU加速分支定界算法
A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem
摘要：
分支定界算法是求解最优性组合优化问题的一种时间密集型的基于树的探索方法。在本文中，我们研究使用GPU计算作为一个主要的补充方式，以加快这些方法。重点放在B&B算法的边界机制，这是他们的探索过程中最耗时的部分。我们提出了一种基于GPU加速边界模型的并行B&B算法。所提出的方法专注于优化数据访问管理，以进一步提高绑定机制的性能，该机制使用不完全适合GPU内存的大型和中间数据集。广泛的实验的贡献已经进行了众所周知的FSP基准使用Nvidia特斯拉C2050 GPU卡。我们得到的性能进行了比较，以一个单一的和多线程的CPU为基础的执行。对于大型问题实例，可实现高达x100的加速。
英文摘要：
Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major complementary way to speed up those methods. The focus is put on the bounding mechanism of B&B algorithms, which is the most time consuming part of their exploration process. We propose a parallel B&B algorithm based on a GPU-accelerated bounding model. The proposed approach concentrate on optimizing data access management to further improve the performance of the bounding mechanism which uses large and intermediate data sets that do not completely fit in GPU memory. Extensive experiments of the contribution have been carried out on well known FSP benchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtained performances to a single and a multithreaded CPU-based execution. Accelerations up to x100 are achieved for large problem instances.
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