论文标题
标准与统一的二进制搜索及其在学习的静态索引中的变体：在排序数据基准软件平台上进行搜索的情况
Standard Vs Uniform Binary Search and Their Variants in Learned Static Indexing: The Case of the Searching on Sorted Data Benchmarking Software Platform
论文作者
论文摘要
学习的索引是在排序表中进行搜索的新方法。一个模型用于预测搜索的间隔，并使用二进制搜索例程来最终确定搜索。他们非常有效。通常，对于最后阶段，通常使用标准C ++库的较低程序例程，尽管这更像是自然的选择，而不是需求。但是，最近不使用机器学习预测的研究表明，二进制搜索或变体的其他实现，即K-ary搜索，更适合利用现代计算机体系结构提供的功能。通过在SOSD中使用的SOSD学习索引基准软件的搜索，我们调查了如何在学习索引中选择搜索程序的搜索例程。我们的结果提供了比下_bound例程更好的选择。我们还强调了这种选择如何取决于要使用的计算机架构。总体而言，我们的发现为在学习的索引框架内选择搜索程序提供了新的和急需的指南。
Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an interval in which to search into and a Binary Search routine is used to finalize the search. They are quite effective. For the final stage, usually, the lower_bound routine of the Standard C++ library is used, although this is more of a natural choice rather than a requirement. However, recent studies, that do not use Machine Learning predictions, indicate that other implementations of Binary Search or variants, namely k-ary Search, are better suited to take advantage of the features offered by modern computer architectures. With the use of the Searching on Sorted Sets SOSD Learned Indexing benchmarking software, we investigate how to choose a Search routine for the final stage of searching in a Learned Index. Our results provide indications that better choices than the lower_bound routine can be made. We also highlight how such a choice may be dependent on the computer architecture that is to be used. Overall, our findings provide new and much-needed guidelines for the selection of the Search routine within the Learned Indexing framework.
