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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
13982
Venue
VLDB
Year
2025
Pagerank
5.9895288e-05
Overall Rank
6,065 | 58.39%
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 6 of 6 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
29 Database Architecture Optimized for the New Bottleneck: Memory Access 1999 VLDB 0.00052093615
823 The Yin and Yang of Processing Data Warehousing Queries on GPU Devices 2013 VLDB 0.00013792901
912 Hardware-Oblivious Parallelism for In-Memory Column-Stores 2013 VLDB 0.00013269804
1,466 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 0.0001068941
1,574 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 0.00010321274
1,977 HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines 2019 VLDB 9.3641101e-05
2,504 Concurrent Analytical Query Processing with GPUs 2014 VLDB 8.4963369e-05
2,566 Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects 2020 SIGMOD 8.4116562e-05
2,736 Efficient Execution of Joins in a Star Schema 2002 SIGMOD 8.1891265e-05
2,809 Text2SQL is Not Enough: Unifying AI and Databases with TAG 2025 CIDR 8.0994951e-05
2,823 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.0893814e-05
3,134 Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment 2021 VLDB 7.7231028e-05
3,227 MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures 2021 SIGMOD 7.6217889e-05
4,021 PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort 2015 VLDB 6.9501893e-05
4,089 TCUDB: Accelerating Database with Tensor Processors 2022 SIGMOD 6.9096857e-05
4,243 Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS 2022 VLDB 6.8073248e-05
4,376 Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources 2017 VLDB 6.7364442e-05
4,535 Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects 2022 SIGMOD 6.6419266e-05
5,092 Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware 2014 VLDB 6.3669454e-05
5,422 GPU Database Systems Characterization and Optimization 2024 VLDB 6.2245373e-05
5,989 Are You Sure You Want to Use MMAP in Your Database Management System? 2022 CIDR 6.0175059e-05
6,774 Evaluating Multi-GPU Sorting with Modern Interconnects 2022 SIGMOD 5.7778738e-05
7,113 GPUQP: Query Co-Processing Using Graphics Processors 2007 SIGMOD 5.6985502e-05
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