16 citations · 36 across the 7 of their papers we have counts for
9 papers
ParGeo: A Library for Parallel Computational Geometry
Yiqiu Wang, Rahul Yesantharao, Shangdi Yu +3
This paper presents ParGeo, a multicore library for computational geometry. ParGeo contains modules for fundamental tasks including d-tree based spatial search, spatial graph ge…
Parallel Batch-Dynamic d-Trees
Rahul Yesantharao, Yiqiu Wang, Laxman Dhulipala +1
d-trees are widely used in parallel databases to support efficient neighborhood/similarity queries. Supporting parallel updates to d-trees is therefore an important operation…
ParChain: A Framework for Parallel Hierarchical Agglomerative Clustering using Nearest-Neighbor Chain
Shangdi Yu, Yiqiu Wang, Yan Gu +2
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain…
Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering
Yiqiu Wang, Shangdi Yu, Yan Gu +1
This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN). Our approach is based on gene…
A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem
Yiqiu Wang, Shangdi Yu, Yan Gu +1
We propose a theoretically-efficient and practical parallel batch-dynamic data structure for the closest pair problem. Our solution is based on a serial dynamic closest pair data s…
Theoretically-Efficient and Practical Parallel DBSCAN
Yiqiu Wang, Yan Gu, Julian Shun
The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for D…
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Tharun Medini, Qixuan Huang, Yiqiu Wang +2
In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However…
Extreme Classification in Log Memory
Qixuan Huang, Yiqiu Wang, Tharun Medini +1
We present Merged-Averaged Classifiers via Hashing (MACH) for K-classification with ultra-large values of K. Compared to traditional one-vs-all classifiers that require O(Kd) memor…
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
Yiqiu Wang, Anshumali Shrivastava, Jonathan Wang +1
We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for \textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search system for ultra-high dimensional…