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Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture
Summary: Revisits hash joins on coupled CPU-GPU architectures, exploiting on-chip cache reuse and fine-grained co-processing with and without partitioning. Extends cost models to auto-guide design choices; on AMD APUs, achieves 53% CPU-only, 35% GPU-only, and 28% conventional co-processing gains.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 10750
- Venue
- VLDB
- Year
- 2013
- Pagerank
- 8.6078505e-05
- Overall Rank
- 2,519 | 82.48%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 26 of 26 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,040 |
A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics |
2020 |
SIGMOD |
9.7057698e-05 |
| 2,067 |
HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics |
2016 |
VLDB |
9.6392739e-05 |
| 3,305 |
Robust Query Processing in Co-Processor-accelerated Databases |
2016 |
SIGMOD |
7.2460965e-05 |
| 3,327 |
Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects |
2020 |
SIGMOD |
7.2205738e-05 |
| 3,465 |
GPL: A GPU-based Pipelined Query Processing Engine |
2016 |
SIGMOD |
7.0695873e-05 |
| 3,762 |
SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures |
2016 |
SIGMOD |
6.7804471e-05 |
| 3,898 |
Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment |
2021 |
VLDB |
6.6551268e-05 |
| 3,993 |
Improving Main Memory Hash Joins on Intel Xeon Phi Processors: An Experimental Approach |
2015 |
VLDB |
6.5534805e-05 |
| 4,002 |
MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures |
2021 |
SIGMOD |
6.545665e-05 |
| 4,085 |
In-Cache Query Co-Processing on Coupled CPU-GPU Architectures |
2015 |
VLDB |
6.4620277e-05 |
| 4,363 |
Hardware-conscious Query Processing in GPU-accelerated Analytical Engines |
2019 |
CIDR |
6.2552614e-05 |
| 4,678 |
OmniDB: Towards Portable and Efficient Query Processing on Parallel CPU/GPU Architectures |
2013 |
VLDB |
6.0046271e-05 |
| 5,019 |
Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS |
2022 |
VLDB |
5.7559197e-05 |
| 5,040 |
Tile-based Lightweight Integer Compression in GPU |
2022 |
SIGMOD |
5.7425187e-05 |
| 5,088 |
TCUDB: Accelerating Database with Tensor Processors |
2022 |
SIGMOD |
5.7072189e-05 |
| 5,247 |
Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects |
2022 |
SIGMOD |
5.6057839e-05 |
| 5,814 |
Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration |
2016 |
SIGMOD |
5.3167137e-05 |
| 6,223 |
Distributed GPU Joins on Fast RDMA-capable Networks |
2023 |
SIGMOD |
5.1496398e-05 |
| 6,369 |
Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs |
2021 |
VLDB |
5.0936663e-05 |
| 6,964 |
A Morsel-Driven Query Execution Engine for Heterogeneous Multi-Cores |
2019 |
VLDB |
4.8815971e-05 |
| 7,038 |
Demonstrating Efficient Query Processing in Heterogeneous Environments |
2014 |
SIGMOD |
4.8546906e-05 |
| 7,568 |
Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs |
2025 |
VLDB |
4.7084322e-05 |
| 7,751 |
Efficiently Processing Joins and Grouped Aggregations on GPUs |
2025 |
SIGMOD |
4.6603427e-05 |
| 8,846 |
Scaling your Hybrid CPU-GPU DBMS to Multiple GPUs |
2024 |
VLDB |
4.4372012e-05 |
| 10,253 |
Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization |
2026 |
VLDB |
4.1945683e-05 |
| 11,020 |
Accelerating Merkle Patricia Trie with GPU |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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