SwiftSpatial: Spatial Joins on Modern Hardware
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909550267858944 |
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| author | Jiang, Wenqi Khavrona, Oleh-Yevhen Parvanov, Martin Alonso, Gustavo |
| author_facet | Jiang, Wenqi Khavrona, Oleh-Yevhen Parvanov, Martin Alonso, Gustavo |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_16520 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SwiftSpatial: Spatial Joins on Modern Hardware Jiang, Wenqi Khavrona, Oleh-Yevhen Parvanov, Martin Alonso, Gustavo Databases Hardware Architecture 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). |
| title | SwiftSpatial: Spatial Joins on Modern Hardware |
| topic | Databases Hardware Architecture |
| url | https://arxiv.org/abs/2309.16520 |