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Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics

Summary: Vortex enables GPU analytics beyond device memory by aggregating PCIe bandwidth across multi-GPU systems, routing data through idle GPUs. Its decoupled kernel/IO programming model and zero-copy late materialization deliver 5.7× over Proteus without GPU caching. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hf6036ac4fc149cc0
Venue
VLDB
Year
2025
Pagerank
6.0902254e-05
Overall Rank
5,541 | 62.75%
DOI
10.14778/3717755.3717780

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yuan_vldb25,
        title = {{Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics}},
        author = {Yuan, Yichao and Iyer, Advait and Ma, Lin and Talati, Nishil},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {4},
        pages = {1250--1263},
        doi = {10.14778/3717755.3717780},
        url = {https://doi.org/10.14778/3717755.3717780},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

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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.

Rank Cited Paper Year Venue Pagerank
27 Database Architecture Optimized for the New Bottleneck: Memory Access 1999 VLDB 0.0005158963
771 The Yin and Yang of Processing Data Warehousing Queries on GPU Devices 2013 VLDB 0.00014085862
856 Hardware-Oblivious Parallelism for In-Memory Column-Stores 2013 VLDB 0.00013428547
1,268 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 0.0001126007
1,533 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 0.00010332035
1,747 HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines 2019 VLDB 9.7335416e-05
2,284 Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects 2020 SIGMOD 8.6954168e-05
2,460 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4348335e-05
2,466 Concurrent Analytical Query Processing with GPUs 2014 VLDB 8.4222621e-05
2,606 Text2SQL is Not Enough: Unifying AI and Databases with TAG 2025 CIDR 8.2295646e-05
2,732 Efficient Execution of Joins in a Star Schema 2002 SIGMOD 8.0822671e-05
2,886 Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment 2021 VLDB 7.9081605e-05
3,053 MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures 2021 SIGMOD 7.7019663e-05
3,626 Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects 2022 SIGMOD 7.1524537e-05
3,888 Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS 2022 VLDB 6.945725e-05
3,972 TCUDB: Accelerating Database with Tensor Processors 2022 SIGMOD 6.8881464e-05
4,065 PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort 2015 VLDB 6.8227646e-05
4,199 Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources 2017 VLDB 6.7410637e-05
4,480 GPU Database Systems Characterization and Optimization 2024 VLDB 6.5807917e-05
5,132 Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware 2014 VLDB 6.2597754e-05
5,160 Are You Sure You Want to Use MMAP in Your Database Management System? 2022 CIDR 6.2482143e-05
6,414 Evaluating Multi-GPU Sorting with Modern Interconnects 2022 SIGMOD 5.7927521e-05
7,188 GPUQP: Query Co-Processing Using Graphics Processors 2007 SIGMOD 5.5908064e-05
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