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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >GPU Strategies for Distance-Based Outlier Detection
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GPU Strategies for Distance-Based Outlier Detection

机译：基于距离的离群值检测的GPU策略

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摘要

The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours, known as “data mining functions.” Among these functions, outlier detection discovers observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is however computationally very demanding and currently requires high-performance computing facilities. We propose a family of parallel and distributed algorithms for graphic processing units (GPU) derived from two distance-based outlier detection algorithms: BruteForce and SolvingSet. The algorithms differ in the way they exploit the architecture and memory hierarchy of the GPU and guarantee significant improvements with respect to the CPU versions, both in terms of scalability and exploitation of parallelism. We provide a detailed discussion of their computational properties and measure performances with an extensive experimentation, comparing the several implementations and showing significant speedups.
机译：在可能很大的大型数据集中发现有趣的模式的过程称为数据挖掘，并且可以通过多种方式执行，称为“数据挖掘功能”。在这些功能中，离群检测发现的观测值与其余数据有很大的出入，并且具有许多重要的实际应用。但是，在非常大的数据集中检测异常值对计算的要求很高，目前需要高性能的计算工具。我们为图形处理单元（GPU）提出了一系列并行和分布式算法，该算法从两种基于距离的离群值检测算法：BruteForce和SolvingSet中获得。这些算法在利用GPU的体系结构和内存层次结构上的方式不同，并且在可扩展性和并行性利用方面都保证了CPU版本方面的显着改进。我们通过广泛的实验详细讨论了它们的计算属性和性能，比较了几种实现并显示出显着的加速效果。

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