G-CasDec: General Cascaded Decompression on GPUs

Summary: G-CasDec enables generic, extensible cascaded decompression on GPUs, balancing aggressive compression of variable-length data with efficient execution via aligned batching, resource reuse, and bytecode orchestration. It achieves 2.8× higher compression ratios and 2.0× faster decompression than state-of-the-art designs. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID: h83b7d8232fc36a52
Venue: SIGMOD
Year: 2027
Pagerank: 4.9605343e-05
Overall Rank: 10,399 | 30.30%
DOI: 10.1145/3837114
PDF: Download (CC BY 4.0)

Incoming Non-self Citations Over Time

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Authors

1. Yongqi Zhuo (Tsinghua University)
2. Xinyu Zeng (Tsinghua University)
3. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
4. Mingyu Gao (Shanghai Qi Zhi Institute; Tsinghua University)

BibTeX Citation

@inproceedings{zhuo_sigmod27,
        title = {{G-CasDec: General Cascaded Decompression on GPUs}},
        author = {Zhuo, Yongqi and Zeng, Xinyu and Zhang, Huanchen and Gao, Mingyu},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837114},
        url = {https://dl.acm.org/doi/10.1145/3837114},
        year = {2027}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
12	C-Store: A Column-oriented DBMS	2005	VLDB	0.00068985264
61	Integrating Compression and Execution in Column-Oriented Database Systems	2006	SIGMOD	0.00039323387
161	DB2 with BLU Acceleration: So Much More than Just a Column Store	2013	VLDB	0.00027629724
748	The Yin and Yang of Processing Data Warehousing Queries on GPU Devices	2013	VLDB	0.00014258503
1,228	A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics	2020	SIGMOD	0.00011425748
2,104	Database Compression on Graphics Processors	2010	VLDB	9.0262021e-05
2,239	BtrBlocks: Efficient Columnar Compression for Data Lakes	2023	SIGMOD	8.7753778e-05
2,811	ALP: Adaptive Lossless floating-Point Compression	2023	SIGMOD	7.9587454e-05
3,063	The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar Code	2023	VLDB	7.6806166e-05
3,471	Tile-based Lightweight Integer Compression in GPU	2022	SIGMOD	7.258594e-05
3,809	An Empirical Evaluation of Columnar Storage Formats	2024	VLDB	6.9890843e-05
4,164	GPU Database Systems Characterization and Optimization	2024	VLDB	6.7560016e-05
4,373	A Deep Dive into Common Open Formats for Analytical DBMSs	2023	VLDB	6.6191786e-05
5,721	Efficiently Processing Joins and Grouped Aggregations on GPUs	2025	SIGMOD	6.0008904e-05
7,771	The FastLanes File Format	2025	VLDB	5.4390723e-05
8,235	Rethinking Analytical Processing in the GPU Era	2026	CIDR	5.3569644e-05
8,578	F3: The Open-Source Data File Format for the Future	2026	SIGMOD	5.2966191e-05

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