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首页> 外文期刊>EURO journal of transportation and logistics >GPU computing in discrete optimization. Part Ⅱ: Survey focused on routing problems
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GPU computing in discrete optimization. Part Ⅱ: Survey focused on routing problems

机译：离散优化中的GPU计算。第二部分：针对路由问题的调查

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摘要

In many cases there is still a large gap between the performance of current optimization technology and the requirements of real-world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low-cost, easily accessible heterogeneous environment for high-performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the second in a series of two. Part I gives a tutorial style introduction to modern PC architectures and GPU programming. Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. We assume that the reader is familiar with GPU programming, and refer the interested reader to Part I. We conclude with lessons learnt, directions for future research, and prospects.
机译：在许多情况下，当前优化技术的性能与实际应用程序的要求之间仍然存在很大差距。与过去一样，通过结合使用更强大的解决方案方法和提高计算机的整体性能，性能将得到改善。这些因素不是独立的。由于物理限制，硬件开发不再导致顺序算法的速度提高，而是并行性提高。现代的商用PC包括一个多核CPU和至少一个GPU，从而为高性能计算提供了一种低成本，易于访问的异构环境。需要结合任务并行化和流处理的新解决方案方法，以充分利用现代计算机体系结构并从未来的硬件开发中获利。本文是两篇系列文章中的第二篇。第一部分对现代PC架构和GPU编程进行了教程风格的介绍。第二部分对针对现代PC的离散优化中的并行计算进行了广泛的文献研究，重点关注路由问题。我们假设读者熟悉GPU编程，并将感兴趣的读者带到第一部分。我们以总结的经验教训，未来研究的方向和前景作为结束。

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