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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
- 6.2552614e-05
- Overall Rank
- 4,363 | 69.65%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,651 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3694317e-05 |
| 3,327 |
Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects |
2020 |
SIGMOD |
7.2205738e-05 |
| 4,002 |
MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures |
2021 |
SIGMOD |
6.545665e-05 |
| 5,019 |
Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS |
2022 |
VLDB |
5.7559197e-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 |
| 6,369 |
Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs |
2021 |
VLDB |
5.0936663e-05 |
| 6,496 |
GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP |
2024 |
SIGMOD |
5.0413077e-05 |
| 6,861 |
HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics |
2023 |
CIDR |
4.905263e-05 |
| 7,209 |
GPU-accelerated data management under the test of time |
2020 |
CIDR |
4.7996023e-05 |
| 7,811 |
Hardware-Oblivious SIMD Parallelism for In-Memory Column-Stores |
2020 |
CIDR |
4.6445165e-05 |
| 8,716 |
nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems |
2024 |
VLDB |
4.4618187e-05 |
| 8,846 |
Scaling your Hybrid CPU-GPU DBMS to Multiple GPUs |
2024 |
VLDB |
4.4372012e-05 |
| 9,692 |
GHive: A Demonstration of GPU-Accelerated Query Processing in Apache Hive |
2022 |
SIGMOD |
4.302852e-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 |
0.00076197749 |
| 52 |
Database Architecture Optimized for the new Bottleneck: Memory Access |
1999 |
VLDB |
0.00066474881 |
| 81 |
Cache Conscious Algorithms for Relational Query Processing |
1994 |
VLDB |
0.00055548574 |
| 113 |
Encapsulation of Parallelism in the Volcano Query Processing System |
1990 |
SIGMOD |
0.00046764513 |
| 343 |
Implementing Database Operations Using SIMD Instructions |
2002 |
SIGMOD |
0.00026768139 |
| 404 |
Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited |
2014 |
VLDB |
0.00024143076 |
| 418 |
Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age |
2014 |
SIGMOD |
0.00023729211 |
| 540 |
Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs |
2011 |
SIGMOD |
0.0002063443 |
| 544 |
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources |
2018 |
SIGMOD |
0.00020521965 |
| 958 |
Rethinking SIMD Vectorization for In-Memory Databases |
2015 |
SIGMOD |
0.00015045316 |
| 1,273 |
The Yin and Yang of Processing Data Warehousing Queries on GPU Devices |
2013 |
VLDB |
0.00012912938 |
| 1,287 |
Hardware-Oblivious Parallelism for In-Memory Column-Stores |
2013 |
VLDB |
0.00012820443 |
| 1,804 |
An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory |
2016 |
SIGMOD |
0.00010501185 |
| 2,014 |
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware |
2016 |
VLDB |
9.7904029e-05 |
| 2,287 |
Pipelined Query Processing in Coprocessor Environments |
2018 |
SIGMOD |
9.0972606e-05 |
| 2,519 |
Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture |
2013 |
VLDB |
8.6078505e-05 |
| 2,651 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3694317e-05 |
| 3,151 |
A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs |
2017 |
SIGMOD |
7.4720668e-05 |
| 3,219 |
Interleaving with Coroutines: A Practical Approach for Robust Index Joins |
2018 |
VLDB |
7.3550716e-05 |
| 3,305 |
Robust Query Processing in Co-Processor-accelerated Databases |
2016 |
SIGMOD |
7.2460965e-05 |
| 3,465 |
GPL: A GPU-based Pipelined Query Processing Engine |
2016 |
SIGMOD |
7.0695873e-05 |
| 4,326 |
Fast Queries Over Heterogeneous Data Through Engine Customization |
2016 |
VLDB |
6.288323e-05 |
| 4,770 |
The Case For Heterogeneous HTAP |
2017 |
CIDR |
5.9338845e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,999 |
Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources |
2017 |
VLDB |
5.7752801e-05 |
| 2,519 |
Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture |
2013 |
VLDB |
8.6078505e-05 |
| 3,465 |
GPL: A GPU-based Pipelined Query Processing Engine |
2016 |
SIGMOD |
7.0695873e-05 |
| 4,770 |
The Case For Heterogeneous HTAP |
2017 |
CIDR |
5.9338845e-05 |
| 2,651 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3694317e-05 |
| 7,038 |
Demonstrating Efficient Query Processing in Heterogeneous Environments |
2014 |
SIGMOD |
4.8546906e-05 |
| 7,377 |
GPUQP: Query Co-Processing Using Graphics Processors |
2007 |
SIGMOD |
4.7484565e-05 |
| 2,330 |
Concurrent Analytical Query Processing with GPUs |
2014 |
VLDB |
9.0192228e-05 |
| 3,696 |
Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS |
2013 |
VLDB |
6.834483e-05 |
| 3,305 |
Robust Query Processing in Co-Processor-accelerated Databases |
2016 |
SIGMOD |
7.2460965e-05 |