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Memory efficient parallel algorithm for optimal DAG structure search using direct communication

机译:使用直接通信的DAG结构最佳搜索的内存高效并行算法

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We present a novel parallel algorithm that solves the optimal directed acyclic graph search problem using less memory as compared to existing algorithms. Using a dynamic programming approach, the time and space complexity of the problem is found to be 0(n . 2(n)), where n represents the number of vertices. The previous algorithm uses adjacent communication to efficiently exchange data between computation nodes. However, it consumes too much memory, and thus is capable of solving up to n = 36. Our novel algorithm, ParaOS-DC, employs direct communication to reduce the memory consumption; this is the main cause of the inefficiency of the previous algorithm. Through computational experiments, we confirmed that our proposed algorithm is much faster than the previous algorithm when memory is insufficient. We also succeeded in solving a problem for n = 37 without any constraints, which is the largest problem size solved in literature till date. (C) 2018 Elsevier Inc. All rights reserved.
机译:我们提出了一种新颖的并行算法,与现有算法相比,该算法使用更少的内存即可解决最优有向无环图搜索问题。使用动态规划方法,发现问题的时间和空间复杂度为0(n。2(n)),其中n表示顶点数。先前的算法使用相邻通信来有效地在计算节点之间交换数据。但是,它消耗了过多的内存,因此最多可以解决n =36。我们的新颖算法ParaOS-DC采用直接通信来减少内存消耗。这是先前算法效率低下的主要原因。通过计算实验,我们确认了当内存不足时,我们提出的算法比以前的算法要快得多。我们还成功地解决了无任何约束的n = 37的问题,这是迄今为止文献中解决的最大问题规模。 (C)2018 Elsevier Inc.保留所有权利。

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