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Pipelined Query Processing in Coprocessor Environments

Summary: Introduces a compiler strategy that fuses several operations into a single GPU kernel to exploit GPU parallelism for query processing. This fusion dramatically cuts memory traffic and kernel time, with up to 7.5x fewer memory accesses and 9.5x faster kernels than operator-at-a-time. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h1852d0d4a0abd6e4
Venue
SIGMOD
Year
2018
Pagerank
0.00010327147
Overall Rank
1,534 | 89.70%
DOI
10.1145/3183713.3183734

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{funke_sigmod18,
        title = {{Pipelined Query Processing in Coprocessor Environments}},
        author = {Funke, Henning and Breß, Sebastian and Noll, Stefan and Markl, Volker and Teubner, Jens},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3183734},
        url = {https://dl.acm.org/doi/10.1145/3183713.3183734},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 40 of 40 citing papers.

Rank Citing Paper Year Venue Pagerank
1,269 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 0.00011254742
1,748 HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines 2019 VLDB 9.7289385e-05
2,287 Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects 2020 SIGMOD 8.691301e-05
2,460 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4308406e-05
3,103 On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML 2018 VLDB 7.6456038e-05
3,628 Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects 2022 SIGMOD 7.1490678e-05
3,657 Tile-based Lightweight Integer Compression in GPU 2022 SIGMOD 7.1227107e-05
3,775 Hardware-conscious Query Processing in GPU-accelerated Analytical Engines 2019 CIDR 7.0261504e-05
3,862 GaccO - A GPU-accelerated OLTP DBMS 2022 SIGMOD 6.9623352e-05
3,888 Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS 2022 VLDB 6.942437e-05
3,962 Data-Parallel Query Processing on Non-Uniform Data 2020 VLDB 6.8916038e-05
3,974 TCUDB: Accelerating Database with Tensor Processors 2022 SIGMOD 6.8848857e-05
4,138 The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product 2021 VLDB 6.782996e-05
4,484 GPU Database Systems Characterization and Optimization 2024 VLDB 6.5776765e-05
4,647 Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs 2021 VLDB 6.4850635e-05
5,376 Distributed GPU Joins on Fast RDMA-capable Networks 2023 SIGMOD 6.1555648e-05
5,543 Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics 2025 VLDB 6.0873423e-05
5,616 BOSS - An Architecture for Database Kernel Composition 2024 VLDB 6.0623704e-05
6,153 Efficiently Processing Joins and Grouped Aggregations on GPUs 2025 SIGMOD 5.8669913e-05
6,491 HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics 2023 CIDR 5.7649204e-05
6,518 GPU-accelerated data management under the test of time 2020 CIDR 5.7561332e-05
6,662 A Morsel-Driven Query Execution Engine for Heterogeneous Multi-Cores 2019 VLDB 5.7155423e-05
6,671 Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs 2025 VLDB 5.7125893e-05
6,927 HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 2023 SIGMOD 5.6399587e-05
8,242 Scaling your Hybrid CPU-GPU DBMS to Multiple GPUs 2024 VLDB 5.3684987e-05
8,280 Scaling GPU-Accelerated Databases beyond GPU Memory Size 2025 VLDB 5.3616863e-05
8,501 SPRINTER: A Fast n-ary Join Query Processing Method for Complex OLAP Queries 2020 SIGMOD 5.3285575e-05
8,528 FuseME: Distributed Matrix Computation Engine based on Cuboid-based Fused Operator and Plan Generation 2022 SIGMOD 5.3212942e-05
8,602 A Case for Graphics-driven Query Processing 2023 VLDB 5.3037284e-05
9,401 Themis: A GPU-accelerated Relational Query Execution Engine 2025 VLDB 5.1837077e-05
9,438 Efficiently Joining Large Relations on Multi-GPU Systems 2025 VLDB 5.1761941e-05
9,663 BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach 2023 SIGMOD 5.142891e-05
10,484 L3: A GPU-Native Co-Designed Data Format for Learned Lossless Lightweight Compression 2026 SIGMOD 4.9769913e-05
10,612 TQEx: Tensor-based Query Engine Enhanced by Bridging the Gap 2026 SIGMOD 4.9769913e-05
10,733 Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization 2026 VLDB 4.9769913e-05
10,914 ZipFlow: a Compiler-based Framework to Unleash Compressed Data Movement for Modern GPUs 2026 VLDB 4.9769913e-05
10,946 RayDB: Building Databases with Ray Tracing Cores 2026 VLDB 4.9769913e-05
11,212 cuMatch: A GPU-based Memory-Efficient Worst-case Optimal Join Processing Method for Subgraph Queries with Complex Patterns 2025 SIGMOD 4.9769913e-05
11,351 Towards Designing Future-Proof Data Processing Systems 2025 VLDB 4.9769913e-05
11,573 Accelerating Merkle Patricia Trie with GPU 2024 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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