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Exploiting coarse grained parallelism in conceptual data mining: finding a needle in a haystack as a distributed effort

机译:在概念数据挖掘中利用粗粒度并行性:在大海捞针中寻找分布式工作

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摘要

A parallel implementation of Ganter’s algorithm to calculate concept lattices for Formal Concept Analysis is presented. A benchmark was executed to experimentally determine the algorithm’s performance, including an AMD Athlon64, Intel dual Xeon, and UltraSPARC T1, with respectively 1, 4, and 24 threads in parallel. Two subsets of Cranfield’s collection were chosen as document set. In addition, the theoretically maximum performance was determined. Due to scheduling problems, the performance of the UltraSPARC was disappointing. Two alternate schedulers are proposed to tackle this problem. It is shown that, given a good scheduler, the algorithm can massively exploit multi-threading architectures and so, substantially reduce the computational burden of Formal Concept Analysis.
机译:提出了Ganter算法的并行实现,该算法可为形式概念分析计算概念格。执行了一个基准测试以实验确定该算法的性能,其中包括AMD Athlon64,Intel Dual Xeon和UltraSPARC T1,分别具有1、4和24个并行线程。选择了克兰菲尔德馆藏的两个子集作为文档集。另外,确定了理论上的最大性能。由于调度问题,UltraSPARC的性能令人失望。建议使用两个备用调度程序来解决此问题。结果表明,只要有一个好的调度程序,该算法就可以大量利用多线程体系结构,从而大大减少了形式概念分析的计算负担。

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