Communication Steps for Parallel Query Processing
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Abstract (arXiv)
We consider the problem of computing a relational query q on a large input database of size n, using a large number p of servers. The computation is performed in rounds, and each server can receive only O(n/p1-varepsilon) bits of data, where varepsilon ∈ [0,1] is a parameter that controls replication. We examine how many global communication steps are needed to compute q. We establish both lower and upper bounds, in two settings. For a single round of communication, we give lower bounds in the strongest possible model, where arbitrary bits may be exchanged; we show that any algorithm requires varepsilon ≥ 1-1/τ^*, where τ^* is the fractional vertex cover of the hypergraph of q. We also give an algorithm that matches the lower bound for a specific class of databases. For multiple rounds of communication, we present lower bounds in a model where routing decisions for a tuple are tuple-based. We show that for the class of tree-like queries there exists a tradeoff between the number of rounds and the space exponent varepsilon. The lower bounds for multiple rounds are the first of their kind. Our results also imply that transitive closure cannot be computed in O(1) rounds of communication.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2013.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
11 papers.
- 2024Understanding Transformer Reasoning Capabilities via Graph Algorithmscited by 64
- 2021Correlation Clustering in Constant Many Parallel Roundscited by 15
- 2022Scalable Differentially Private Clustering via Hierarchically Separated Treescited by 10
- 2021Differentially Private Densest Subgraph Detectioncited by 7
- 2021Efficient and Local Parallel Random Walkscited by 2
- 2022Sublinear Algorithms for Hierarchical Clusteringcited by 2
- 2025Fast attention mechanisms: a tale of parallelismcited by 2
- 2024Making Old Things New: A Unified Algorithm for Differentially Private Clusteringcited by 1
- 2022Distributed k-Means with Outliers in General Metricscited by 0
- 2023Massively-Parallel Heat Map Sorting and Applications To Explainable Clusteringcited by 0
- 2026Scalable and Distributed Silhouette Approximationcited by 0
- 2026Scalable and Distributed Silhouette Approximation2 Jul 2026
- 2025Fast attention mechanisms: a tale of parallelism10 Sep 2025
- 2024Making Old Things New: A Unified Algorithm for Differentially Private Clustering17 Jun 2024
- 2024Understanding Transformer Reasoning Capabilities via Graph Algorithms28 May 2024
- 2023Massively-Parallel Heat Map Sorting and Applications To Explainable Clustering14 Sep 2023
- 2022Scalable Differentially Private Clustering via Hierarchically Separated Trees17 Jun 2022
- 2022Sublinear Algorithms for Hierarchical Clustering15 Jun 2022
- 2022Distributed k-Means with Outliers in General Metrics16 Feb 2022
- 2021Efficient and Local Parallel Random Walks1 Dec 2021
- 2021Correlation Clustering in Constant Many Parallel Rounds15 Jun 2021
- 2021Differentially Private Densest Subgraph Detection27 May 2021