以Dilworth数为参数的多命中集的串行和并行核化,在GPU上实现

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-09-12 DOI:10.1016/j.jcss.2023.103479
René van Bevern , Artem M. Kirilin , Daniel A. Skachkov , Pavel V. Smirnov , Oxana Yu. Tsidulko
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引用次数: 1

摘要

NP困难的多重命中集问题是找到与给定输入集合中的每个集相交给定次数的最小基数集的问题。推广了Weihe的一个著名的数据约简算法,我们给出了由Dilworth数参数化的多重打击集的问题核。Dilworth数是Foldes和Hammer于1978年引入的一个图参数,但在参数化复杂性理论的背景下似乎迄今尚未探索。利用矩阵乘法,将算法加速到二次序列时间和对数并行时间。我们通过实验评估了我们的算法。通过在GPU上实现我们的算法,我们展示了在SIMD(单指令,多数据)架构上实现核化算法的可行性。
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Serial and parallel kernelization of Multiple Hitting Set parameterized by the Dilworth number, implemented on the GPU

The NP-hard Multiple Hitting Set problem is the problem of finding a minimum-cardinality set intersecting each of the sets in a given input collection a given number of times. Generalizing a well-known data reduction algorithm due to Weihe, we show a problem kernel for Multiple Hitting Set parameterized by the Dilworth number, a graph parameter introduced by Foldes and Hammer in 1978 yet seemingly so far unexplored in the context of parameterized complexity theory. Using matrix multiplication, we speed up the algorithm to quadratic sequential time and logarithmic parallel time. We experimentally evaluate our algorithms. By implementing our algorithm on GPUs, we show the feasibility of realizing kernelization algorithms on SIMD (Single Instruction, Multiple Data) architectures.

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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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