Statically Optimal Binary Search Tree Computation Using Non-Serial Polyadic Dynamic Programming on GPU's

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS International Journal of Grid and High Performance Computing Pub Date : 2019-01-01 DOI:10.4018/IJGHPC.2019010104
Mohsin Altaf Wani, Manzoor Ahmad
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引用次数: 1

Abstract

Modern GPUs perform computation at a very high rate when compared to CPUs; as a result, they are increasingly used for general purpose parallel computation. Determining if a statically optimal binary search tree is an optimization problem to find the optimal arrangement of nodes in a binary search tree so that average search time is minimized. Knuth's modification to the dynamic programming algorithm improves the time complexity to O(n2). We develop a multiple GPU-based implementation of this algorithm using different approaches. Using suitable GPU implementation for a given workload provides a speedup of up to four times over other GPU based implementations. We are able to achieve a speedup factor of 409 on older GTX 570 and a speedup factor of 745 is achieved on a more modern GTX 1060 when compared to a conventional single threaded CPU based implementation.
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GPU上非串行多进动态规划的静态最优二叉搜索树计算
与cpu相比,现代gpu以非常高的速率执行计算;因此,它们越来越多地用于通用并行计算。确定静态最优二叉搜索树是否是一个优化问题,以找到二叉搜索树中节点的最优排列,从而使平均搜索时间最小化。Knuth对动态规划算法的改进将时间复杂度提高到O(n2)。我们使用不同的方法开发了基于多个gpu的算法实现。对于给定的工作负载,使用合适的GPU实现可以提供比其他基于GPU的实现高达四倍的加速。与传统的基于单线程CPU的实现相比,我们能够在较旧的GTX 570上实现409的加速因子,在更现代的GTX 1060上实现745的加速因子。
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来源期刊
CiteScore
1.70
自引率
10.00%
发文量
24
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