论文标题
迈向平行学习排序
Towards Parallel Learned Sorting
论文作者
论文摘要
我们引入了一种新的分类算法，该算法是ML增强分类与现场超级标量样品排序（IPS4O）的组合。我们工作的主要贡献是实现并行ML增强分类，因为以前的算法仅限于顺序实现。我们介绍了原位的平行学习排序（IPLS）算法，并将其与其他分类方法进行了广泛的比较。 IPLS将IPS4O框架与使用最快的最小冲突程度算法训练的线性模型结合在一起。实验结果并不是最快的算法加冕IPL。但是，它们确实表明IPL在同行中具有竞争力，并且使用IPS4O框架是一种有前途的平行学习分类方法。
We introduce a new sorting algorithm that is the combination of ML-enhanced sorting with the In-place Super Scalar Sample Sort (IPS4o). The main contribution of our work is to achieve parallel ML-enhanced sorting, as previous algorithms were limited to sequential implementations. We introduce the In-Place Parallel Learned Sort (IPLS) algorithm and compare it extensively against other sorting approaches. IPLS combines the IPS4o framework with linear models trained using the Fastest Minimum Conflict Degree algorithm to partition data. The experimental results do not crown IPLS as the fastest algorithm. However, they do show that IPLS is competitive among its peers and that using the IPS4o framework is a promising approach towards parallel learned sorting.
