SwiftSpatial: Spatial Joins on Modern Hardware

Spatial joins are among the most time-consuming spatial queries, remaining costly even in parallel and distributed systems. In this paper, we explore hardware acceleration for spatial joins by proposing SwiftSpatial, an FPGA-based accelerator that can be deployed in data centers and at the edge. SwiftSpatial contains multiple high-performance join units with innovative hybrid parallelism, several efficient memory management units, and an extensible on-chip join scheduler that supports the popular R-tree synchronous traversal and partition-based spatial-merge (PBSM) algorithms. Benchmarked against various CPU and GPU-based spatial data processing systems, SwiftSpatial demonstrates a latency reduction of up to 41.03x relative to the best-performing baseline, while requiring 6.16x less power. The performance and energy efficiency of SwiftSpatial demonstrate its potential to be used in a variety of configurations (e.g., as an accelerator, near storage, in-network) as well as on different devices (e.g., data centers where FPGAs are widely available or mobile devices, which also contain FPGAs for specialized processing).

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2025-06-17
DOI
https://doi.org/10.1145/3725361
Citations
1
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
1.49
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article

SwiftSpatial: Spatial Joins on Modern Hardware

Wenqi Jiang, Martin Parvanov, Gustavo Alonso, عبدالله باقر بوكلاه
1 citations
Proceedings of the ACM on Management of Data
Data Management and Algorithms
1.49
article

SwiftSpatial: Spatial Joins on Modern Hardware

Wenqi Jiang, Martin Parvanov, Gustavo Alonso, عبدالله باقر بوكلاه
article en
1 citations

Abstract

Spatial joins are among the most time-consuming spatial queries, remaining costly even in parallel and distributed systems. In this paper, we explore hardware acceleration for spatial joins by proposing SwiftSpatial, an FPGA-based accelerator that can be deployed in data centers and at the edge. SwiftSpatial contains multiple high-performance join units with innovative hybrid parallelism, several efficient memory management units, and an extensible on-chip join scheduler that supports the popular R-tree synchronous traversal and partition-based spatial-merge (PBSM) algorithms. Benchmarked against various CPU and GPU-based spatial data processing systems, SwiftSpatial demonstrates a latency reduction of up to 41.03x relative to the best-performing baseline, while requiring 6.16x less power. The performance and energy efficiency of SwiftSpatial demonstrate its potential to be used in a variety of configurations (e.g., as an accelerator, near storage, in-network) as well as on different devices (e.g., data centers where FPGAs are widely available or mobile devices, which also contain FPGAs for specialized processing).

Proceedings of the ACM on Management of DataVol. 3(3)
ETH Zurich (CH)
Affordable and clean energy
Openalex Percentile: Top 18%
Data Management and Algorithms
1.49
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SwiftSpatial: Spatial Joins on Modern Hardware — Wenqi Jiang, Martin Parvanov, et al. · Proceedings of the ACM on Management of Data (2025) | TGRS Research Map | TGRS