一种基于自适应多GPU的分支定界算法。Flow-Shop调度问题的实例研究
An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem
摘要：
使用分支定界（B&B）算法精确求解组合优化问题（COP）需要大量的计算资源。因此，我们最近研究了使用并行边界模型在图形处理单元（GPU）上设计B&B算法。所提出的模型假设并行评估的子问题的池上的下限。结果表明，评估池的大小对B&B的性能有显着的影响，它强烈地依赖于所解决的问题实例。在本文中，我们设计了一个自适应的并行B&B算法，用于解决基于置换的组合优化问题，如FSP（流水车间调度问题）的GPU加速器。为此，我们提出了一个动态的启发式参数自动调整在运行时。这项工作的另一个挑战是通过使用多个GPU设备的组合计算能力来利用更大程度的并行性。该方法已被应用到置换流水车间问题。广泛的实验已经进行了著名的FSP基准测试，使用Nvidia Tesla S1070计算系统配备了两个Tesla T10 GPU。与基于CPU的执行相比，对于大型问题实例，可实现高达105的加速。
英文摘要：
Solving exactly Combinatorial Optimization Problems (COPs) using a Branch-and-Bound (B&B) algorithm requires a huge amount of computational resources. Therefore, we recently investigated designing B&B algorithms on top of graphics processing units (GPUs) using a parallel bounding model. The proposed model assumes parallelizing the evaluation of the lower bounds on pools of sub-problems. The results demonstrated that the size of the evaluated pool has a significant impact on the performance of B&B and that it depends strongly on the problem instance being solved. In this paper, we design an adaptative parallel B&B algorithm for solving permutation-based combinatorial optimization problems such as FSP (Flow-shop Scheduling Problem) on GPU accelerators. To do so, we propose a dynamic heuristic for parameter auto-tuning at runtime. Another challenge of this work is to exploit larger degrees of parallelism by using the combined computational power of multiple GPU devices. The approach has been applied to the permutation flow-shop problem. Extensive experiments have been carried out on well-known FSP benchmarks using an Nvidia Tesla S1070 Computing System equipped with two Tesla T10 GPUs. Compared to a CPU-based execution, accelerations up to 105 are achieved for large problem instances.
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