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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
322
Venue
CIDR
Year
2019
Pagerank
7.1235328e-05
Overall Rank
3,791 | 74.00%
DOI
-
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{chrysogelos_cidr19,
address = {Amsterdam, Netherlands},
series = {{CIDR} '19},
title = {{Hardware-conscious Query Processing in GPU-accelerated Analytical Engines}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Chrysogelos, Periklis and Sioulas, Panagiotis and Ailamaki, Anastasia},
year = {2019}
}
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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.
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Pagerank
14
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2005
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1999
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Encapsulation of Parallelism in the Volcano Query Processing System
1990
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Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age
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Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited
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Implementing Database Operations Using SIMD Instructions
2002
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360
Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs
2011
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445
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
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Rethinking SIMD Vectorization for In-Memory Databases
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823
The Yin and Yang of Processing Data Warehousing Queries on GPU Devices
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Hardware-Oblivious Parallelism for In-Memory Column-Stores
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An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory
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1,469
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware
2016
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1,574
Pipelined Query Processing in Coprocessor Environments
2018
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0.00010321274
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HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines
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Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture
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2017
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