集群并行 FPT 应用程序

James Cheetham, F. Dehne, A. Rau-Chaplin, U. Stege, Peter J. Taillon
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引用次数: 5

摘要

固定参数可跟踪性(FPT)技术最近成功地解决了具有实际重要性的np完全问题实例,这些实例太大而无法用以前的方法解决。在本文中,我们展示了如何通过增加并行性来增强这种方法,从而允许在实践中解决更大的问题实例。更准确地说,我们展示了并行性在应用于FPT算法的有界树搜索阶段时的潜力。我们将我们的方法应用于k-VERTEX COVER问题,该问题具有重要的应用,例如,在计算生物化学的多序列比对中。我们使用C语言和MPI通信库实现了k-VERTEX COVER问题的并行FPT方法,并在PC集群上进行了测试。这是平行FPT技术的首次实验检验。我们对从国家生物技术信息中心获得的蛋白质序列进行了平行k-VERTEX COVER方法的测试。作为我们实验的一部分,我们解决了比以前报道的任何实现更大的k-VERTEX COVER实例。例如,我们的代码可以在不到1.5小时的时间内解决k /spl / 400的问题实例。由于我们的并行FPT算法只需要处理器之间很少的通信,我们希望我们的方法在网格上也能表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A parallel FPT application for clusters
Fixed-parameter tractability (FPT) techniques have recently been successful in solving NP-complete problem instances of practical importance which were too large to be solved with previous methods. In this paper we show how to enhance this approach through the addition of parallelism, thereby allowing even larger problem instances to be solved in practice. More precisely, we demonstrate the potential of parallelism when applied to the bounded-tree search phase of FPT algorithms. We apply our methodology to the k-VERTEX COVER problem which has important applications, e.g., in multiple sequence alignments for computational biochemistry. We have implemented our parallel FPT method for the k-VERTEX COVER problem using C and the MPI communication library, and tested it on a PC cluster. This is the first experimental examination of parallel FPT techniques. We have tested our parallel k-VERTEX COVER method on protein sequences obtained from the National Center for Biotechnology Information. As part of our experiments, we solved larger instances of k-VERTEX COVER than in any previously reported implementations. For example, our code can solve problem instances with k /spl ges/ 400 in less than 1.5 hours. Since our parallel FPT algorithm requires only very little communication between processors, we expect our method to also perform well on Grids.
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