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ZipFlow: a Compiler-based Framework to Unleash Compressed Data Movement for Modern GPUs

Summary: ZipFlow compiler-optimizes compression, PCIe transfer, and GPU decompression holistically, classifying codecs by parallelism and applying architecture-aware scheduling. It achieves 2.08× lower latency than nvCOMP and up to 3.14× over CPU engines. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
h8726df5ff5486f56
Venue
VLDB
Year
2026
Pagerank
4.9769913e-05
Overall Rank
10,914 | 26.65%
DOI
10.14778/3827998.3828003
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@article{yeo_vldb26,
        title = {{ZipFlow: a Compiler-based Framework to Unleash Compressed Data Movement for Modern GPUs}},
        author = {Yeo, Gwangoo and Shen, Zhiyang and Cui, Wei and Interlandi, Matteo and Sen, Rathijit and Ding, Bailu and Chen, Qi and Rhu, Minsoo},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {12},
        pages = {3888--3901},
        doi = {10.14778/3827998.3828003},
        url = {https://doi.org/10.14778/3827998.3828003},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,932 TQP++: Bridging ML Compilers and Analytical Query Processing on GPUs 2026 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

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

Rank Cited Paper Year Venue Pagerank
616 Relational Joins on Graphics Processors 2008 SIGMOD 0.00015554627
775 The Yin and Yang of Processing Data Warehousing Queries on GPU Devices 2013 VLDB 0.0001407924
857 Hardware-Oblivious Parallelism for In-Memory Column-Stores 2013 VLDB 0.00013422539
1,269 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 0.00011254742
1,534 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 0.00010327147
1,542 HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics 2016 VLDB 0.0001030858
2,187 Database Compression on Graphics Processors 2010 VLDB 8.891124e-05
2,317 BtrBlocks: Efficient Columnar Compression for Data Lakes 2023 SIGMOD 8.6492924e-05
2,460 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4308406e-05
2,487 A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs 2017 SIGMOD 8.3939994e-05
2,887 Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment 2021 VLDB 7.9044174e-05
2,929 ALP: Adaptive Lossless floating-Point Compression 2023 SIGMOD 7.838721e-05
3,182 The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar Code 2023 VLDB 7.5589507e-05
3,657 Tile-based Lightweight Integer Compression in GPU 2022 SIGMOD 7.1227107e-05
3,888 Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS 2022 VLDB 6.942437e-05
3,962 Data-Parallel Query Processing on Non-Uniform Data 2020 VLDB 6.8916038e-05
3,974 TCUDB: Accelerating Database with Tensor Processors 2022 SIGMOD 6.8848857e-05
4,138 The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product 2021 VLDB 6.782996e-05
4,484 GPU Database Systems Characterization and Optimization 2024 VLDB 6.5776765e-05
4,647 Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs 2021 VLDB 6.4850635e-05
4,992 GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP 2024 SIGMOD 6.3217714e-05
5,543 Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics 2025 VLDB 6.0873423e-05
6,153 Efficiently Processing Joins and Grouped Aggregations on GPUs 2025 SIGMOD 5.8669913e-05
6,469 Output-sensitive Conjunctive Query Evaluation 2024 PODS 5.7719911e-05
6,518 GPU-accelerated data management under the test of time 2020 CIDR 5.7561332e-05
8,280 Scaling GPU-Accelerated Databases beyond GPU Memory Size 2025 VLDB 5.3616863e-05
10,025 Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem 2022 VLDB 5.091949e-05
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