Optimal Parallel Algorithms for Computing Single-Linkage Dendrograms

Core Concepts

The authors present two novel deterministic parallel algorithms for efficiently computing the single-linkage dendrogram (SLD) of an input edge-weighted tree. Their algorithms achieve optimal work bounds and significantly outperform the commonly used sequential Union-Find algorithm.

Abstract

The paper focuses on designing efficient parallel algorithms for computing the single-linkage dendrogram (SLD) of an input edge-weighted tree. The key contributions are: A novel merge-based framework for computing SLDs, which allows merging the SLDs of two subtrees that share a single vertex and no edges. An optimal parallel algorithm called SLD-TreeContraction that leverages the merge framework and parallel tree contraction. It achieves O(n log h) work and O(log^2 n log^2 h) depth, where h is the height of the output dendrogram. A second algorithm called ParUF that is a natural parallelization of the sequential Union-Find algorithm. It also achieves the optimal O(n log h) work bound. Theoretical analyses showing the optimality of the work bounds for comparison-based algorithms. Experimental results demonstrating significant speedups of up to 150x over the highly-optimized sequential Union-Find implementation on billion-scale input trees. The authors leverage novel structural insights about SLDs, such as the concept of "characteristic spines", to design their efficient parallel algorithms. The merge-based framework and the use of parallel tree contraction are key to achieving the optimal work bounds.

Stats

The input is an edge-weighted tree with n vertices. The output is the single-linkage dendrogram (SLD) of the input tree, which is a binary tree with n-1 internal nodes. The height of the output SLD is denoted as h, where log n ≤ h ≤ n-1.

Quotes

"Our new algorithms can quickly compute the SLD on billion-scale trees, and obtain up to 150x speedup over the highly-efficient Union-Find algorithm typically used to compute SLDs in practice." "We leverage these structural results to design two novel deterministic parallel single-linkage dendrogram algorithms."

Key Insights Distilled From

Optimal Parallel Algorithms for Dendrogram Computation and Single-Linkage Clustering

by Laxman Dhuli... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19019.pdf

Deeper Inquiries

How can the parallel algorithms be extended to handle dynamic updates to the input tree, such as edge insertions or deletions

To handle dynamic updates to the input tree, such as edge insertions or deletions, the parallel algorithms can be extended by incorporating techniques for maintaining the integrity of the dendrogram structure. For edge insertions, the algorithm can be modified to dynamically update the dendrogram by adding the new edge to the appropriate clusters and recalculating the SLD as needed. This process may involve identifying the affected clusters, updating the characteristic spines in the heaps, and performing the necessary merges to reflect the changes in the tree structure. Similarly, for edge deletions, the algorithm can adjust the dendrogram by removing the deleted edge from the clusters and updating the SLD accordingly. This may require reevaluating the characteristic spines, reorganizing the heap structures, and potentially undoing previous merges that involved the deleted edge. By implementing efficient data structures and algorithms to handle these dynamic updates, the parallel algorithms can maintain the accuracy and consistency of the dendrogram in response to changes in the input tree.

What are the implications of the optimal work bounds on the practical performance of the algorithms, especially for large-scale real-world datasets

The optimal work bounds achieved by the algorithms have significant implications for the practical performance, especially when dealing with large-scale real-world datasets. The 𝑂(𝑛logℎ) work bound ensures that the algorithms can efficiently compute the SLD even for billion-scale trees, making them suitable for handling massive datasets commonly encountered in various applications. This efficiency translates to faster processing times and improved scalability, allowing for the analysis of complex hierarchical structures in a timely manner. In practical terms, the algorithms' speedup over existing methods, such as the Union-Find algorithm, can lead to substantial performance gains. The up to 150x speedup observed in the experiments demonstrates the practical impact of the optimized parallel algorithms in reducing computation time for dendrogram computation. Overall, the optimal work bounds not only enhance the theoretical efficiency of the algorithms but also have tangible benefits for real-world applications, enabling faster and more scalable hierarchical clustering on large datasets.

Can the techniques developed in this paper be applied to other hierarchical clustering problems beyond single-linkage dendrograms

The techniques developed in this paper for computing single-linkage dendrograms can be applied to other hierarchical clustering problems beyond the specific context discussed. The divide-and-conquer framework, merge-based algorithms, and parallel tree contraction strategies can be adapted to address similar clustering tasks that involve hierarchical structures. By leveraging the principles of efficient dendrogram computation, researchers can explore applications in diverse domains where hierarchical clustering is essential for data analysis and pattern recognition. For instance, the approach of maintaining characteristic spines and using parallel heaps for efficient merges can be generalized to handle different types of hierarchical clustering algorithms, such as complete-linkage or average-linkage clustering. The concept of recursively partitioning the input tree, computing cluster merges, and updating the dendrogram can be extended to various clustering techniques that rely on hierarchical relationships among data points. Overall, the methodologies and insights gained from optimizing parallel algorithms for single-linkage dendrograms can be leveraged to enhance the efficiency and scalability of hierarchical clustering solutions in a broader range of clustering problems.