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Hardware-conscious Query Processing in GPU-accelerated Analytical Engines
Summary: HAPE: a blueprint that compiles hardware-aware single-device operators into modules and orchestrates data/control transfers for concurrent multi-CPU multi-GPU analytical query execution. Prototype (radix-join, TPC-H) achieves up to 10× vs CPU, 3.5× vs GPU baselines and 1.6–8× vs commercial DBMSs.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 323
- Venue
- CIDR
- Year
- 2019
- Pagerank
- 6.2493951e-05
- Overall Rank
- 4,359 | 69.71%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,659 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3615158e-05 |
| 3,328 |
Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects |
2020 |
SIGMOD |
7.2136181e-05 |
| 4,000 |
MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures |
2021 |
SIGMOD |
6.5419402e-05 |
| 5,018 |
Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS |
2022 |
VLDB |
5.7503878e-05 |
| 5,089 |
TCUDB: Accelerating Database with Tensor Processors |
2022 |
SIGMOD |
5.7017353e-05 |
| 5,251 |
Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects |
2022 |
SIGMOD |
5.6003972e-05 |
| 6,367 |
Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs |
2021 |
VLDB |
5.0887599e-05 |
| 6,492 |
GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP |
2024 |
SIGMOD |
5.0364695e-05 |
| 6,861 |
HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics |
2023 |
CIDR |
4.9005561e-05 |
| 7,209 |
GPU-accelerated data management under the test of time |
2020 |
CIDR |
4.7949787e-05 |
| 7,817 |
Hardware-Oblivious SIMD Parallelism for In-Memory Column-Stores |
2020 |
CIDR |
4.6400608e-05 |
| 8,706 |
nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems |
2024 |
VLDB |
4.4587004e-05 |
| 8,846 |
Scaling your Hybrid CPU-GPU DBMS to Multiple GPUs |
2024 |
VLDB |
4.432948e-05 |
| 9,691 |
GHive: A Demonstration of GPU-Accelerated Query Processing in Apache Hive |
2022 |
SIGMOD |
4.2987288e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 35 |
MonetDB/X100: Hyper-Pipelining Query Execution |
2005 |
CIDR |
0.00076209479 |
| 52 |
Database Architecture Optimized for the new Bottleneck: Memory Access |
1999 |
VLDB |
0.00066322421 |
| 81 |
Cache Conscious Algorithms for Relational Query Processing |
1994 |
VLDB |
0.00055253195 |
| 113 |
Encapsulation of Parallelism in the Volcano Query Processing System |
1990 |
SIGMOD |
0.0004673401 |
| 343 |
Implementing Database Operations Using SIMD Instructions |
2002 |
SIGMOD |
0.00026756534 |
| 403 |
Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited |
2014 |
VLDB |
0.00024176677 |
| 417 |
Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age |
2014 |
SIGMOD |
0.00023734582 |
| 538 |
Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs |
2011 |
SIGMOD |
0.00020632609 |
| 542 |
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources |
2018 |
SIGMOD |
0.00020522627 |
| 959 |
Rethinking SIMD Vectorization for In-Memory Databases |
2015 |
SIGMOD |
0.00015034808 |
| 1,271 |
The Yin and Yang of Processing Data Warehousing Queries on GPU Devices |
2013 |
VLDB |
0.00012900735 |
| 1,285 |
Hardware-Oblivious Parallelism for In-Memory Column-Stores |
2013 |
VLDB |
0.00012809552 |
| 1,800 |
An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory |
2016 |
SIGMOD |
0.00010494121 |
| 2,019 |
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware |
2016 |
VLDB |
9.7814175e-05 |
| 2,292 |
Pipelined Query Processing in Coprocessor Environments |
2018 |
SIGMOD |
9.0884645e-05 |
| 2,523 |
Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture |
2013 |
VLDB |
8.599693e-05 |
| 2,659 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3615158e-05 |
| 3,161 |
A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs |
2017 |
SIGMOD |
7.4648665e-05 |
| 3,225 |
Interleaving with Coroutines: A Practical Approach for Robust Index Joins |
2018 |
VLDB |
7.3487507e-05 |
| 3,307 |
Robust Query Processing in Co-Processor-accelerated Databases |
2016 |
SIGMOD |
7.2391191e-05 |
| 3,471 |
GPL: A GPU-based Pipelined Query Processing Engine |
2016 |
SIGMOD |
7.0628019e-05 |
| 4,319 |
Fast Queries Over Heterogeneous Data Through Engine Customization |
2016 |
VLDB |
6.2823814e-05 |
| 4,774 |
The Case For Heterogeneous HTAP |
2017 |
CIDR |
5.9279395e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,996 |
Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources |
2017 |
VLDB |
5.7697228e-05 |
| 2,523 |
Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture |
2013 |
VLDB |
8.599693e-05 |
| 3,471 |
GPL: A GPU-based Pipelined Query Processing Engine |
2016 |
SIGMOD |
7.0628019e-05 |
| 4,774 |
The Case For Heterogeneous HTAP |
2017 |
CIDR |
5.9279395e-05 |
| 2,659 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3615158e-05 |
| 7,038 |
Demonstrating Efficient Query Processing in Heterogeneous Environments |
2014 |
SIGMOD |
4.8500241e-05 |
| 7,375 |
GPUQP: Query Co-Processing Using Graphics Processors |
2007 |
SIGMOD |
4.7439007e-05 |
| 2,336 |
Concurrent Analytical Query Processing with GPUs |
2014 |
VLDB |
9.0106308e-05 |
| 3,698 |
Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS |
2013 |
VLDB |
6.8278944e-05 |
| 3,307 |
Robust Query Processing in Co-Processor-accelerated Databases |
2016 |
SIGMOD |
7.2391191e-05 |