递归模型索引的批判性分析
A Critical Analysis of Recursive Model Indexes
摘要：
递归模型索引（RMI）最近被引入作为机器学习的替代传统索引的排序数据，实现了显着的快速查找。后续工作侧重于解释RMI的性能和通过枚举自动配置RMI。不幸的是，配置RMI涉及到设置多个超参数，在实践中枚举这些超参数通常非常耗时。因此，在这项工作中，我们进行了第一次独立于发明人的广泛的RMI分析，目的是了解每个超参数对性能的影响。特别是，我们表明，除了模型类型和层的大小，必须考虑误差界和搜索算法，以达到最佳的性能。根据我们的研究结果，我们开发了一个简单的配置RMI遵循的指导方针。我们评估我们的准则，通过比较所产生的RMI与一些国家的最先进的指标，无论是学习和传统的。我们表明，我们的简单的指导方针是足以实现竞争力的性能与其他学习的索引和RMI的配置是使用昂贵的枚举过程确定的。此外，在仔细重新实施RMI的同时，我们能够将构建时间缩短2.5倍至6.3倍。
英文摘要：
The recursive model index (RMI) has recently been introduced as a machine-learned replacement for traditional indexes over sorted data, achieving remarkably fast lookups. Follow-up work focused on explaining RMI's performance and automatically configuring RMIs through enumeration. Unfortunately, configuring RMIs involves setting several hyperparameters, the enumeration of which is often too time-consuming in practice. Therefore, in this work, we conduct the first inventor-independent broad analysis of RMIs with the goal of understanding the impact of each hyperparameter on performance. In particular, we show that in addition to model types and layer size, error bounds and search algorithms must be considered to achieve the best possible performance. Based on our findings, we develop a simple-to-follow guideline for configuring RMIs. We evaluate our guideline by comparing the resulting RMIs with a number of state-of-the-art indexes, both learned and traditional. We show that our simple guideline is sufficient to achieve competitive performance with other learned indexes and RMIs whose configuration was determined using an expensive enumeration procedure. In addition, while carefully reimplementing RMIs, we are able to improve the build time by 2.5x to 6.3x.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
