面向加速器的时态数据采掘算法转换
Accelerator-Oriented Algorithm Transformation for Temporal Data Mining
摘要：
时态数据采掘算法在包括计算神经科学在内的许多应用领域，特别是在尖峰训练数据的分析中变得越来越重要。虽然应用科学家已经能够很容易地收集多神经元数据集，但由于缺乏强大的算法和无法访问强大的硬件平台，分析能力一直落后。GPU架构（如Nvidia的GTX 280）的出现提供了一个具有成本效益的选择，可以将这些功能带到神经科学家的桌面上。与其将现有的算法移植到这个架构上，我们提倡算法转换的需要，即，重新考虑算法的设计，不必严格反映其串行实现。我们提出了一种新的实现频繁发作发现算法，通过重新审视“在大”的问题，如问题分解以及“在小”的问题，如数据布局和内存访问模式。这是不平凡的，因为频繁的情节发现并不适合GPU友好的数据并行映射策略。许多数据集的应用程序和CPU以及以前的GPU实现的比较展示了我们的方法的优势。
英文摘要：
Temporal data mining algorithms are becoming increasingly important in many application domains including computational neuroscience, especially the analysis of spike train data. While application scientists have been able to readily gather multi-neuronal datasets, analysis capabilities have lagged behind, due to both lack of powerful algorithms and inaccessibility to powerful hardware platforms. The advent of GPU architectures such as Nvidia's GTX 280 offers a cost-effective option to bring these capabilities to the neuroscientist's desktop. Rather than port existing algorithms onto this architecture, we advocate the need for algorithm transformation, i.e., rethinking the design of the algorithm in a way that need not necessarily mirror its serial implementation strictly. We present a novel implementation of a frequent episode discovery algorithm by revisiting "in-the-large" issues such as problem decomposition as well as "in-the-small" issues such as data layouts and memory access patterns. This is non-trivial because frequent episode discovery does not lend itself to GPU-friendly data-parallel mapping strategies. Applications to many datasets and comparisons to CPU as well as prior GPU implementations showcase the advantages of our approach.
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