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Fast Snippet Generation Based on CPU-GPU Hybrid System

机译:基于CPU-GPU混合系统的快速代码片段生成

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摘要

As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single snippet is a lightweight task. However, it will be a heavy workload to generate multiple snippets of multiple documents as the search engines need to process large amount of queries per second, and each result list usually contains several snippets. To deal with this heavy workload, we propose a new high-performance snippet generation approach based on CPU-GPU hybrid system. Our main contribution of this paper is to present a parallel processing stream for large-scale snippet generation tasks using GPU. We adopt a sliding document segmentation method in our approach which costs more computing resources but can avoid the common defect that the high relevant fragment may be cut off. The experimental results show that our approach gains a speedup of nearly 6 times in average process time compared with the baseline approach-Highlighter.
机译:作为搜索结果表示的重要组成部分,基于查询的文档摘要生成已成为搜索引擎的一种流行方法,该方法使结果列表对用户更具参考价值。生成单个代码段是一项轻量级的任务。但是,由于搜索引擎需要每秒处理大量查询,因此生成多个文档的多个摘要将是繁重的工作量,并且每个结果列表通常包含多个摘要。为了应对这种繁重的工作量,我们提出了一种基于CPU-GPU混合系统的新型高性能代码片段生成方法。本文的主要贡献是为使用GPU的大规模代码段生成任务提供了并行处理流。我们在方法中采用了滑动文档分割方法,该方法花费更多的计算资源,但可以避免较高的相关片段可能被切除的常见缺陷。实验结果表明,与基线方法-Highlighter相比,我们的方法在平均处理时间方面的速度提高了近6倍。

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