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Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

Summary: Co-designs Flink’s execution model and hardware backends to transparently accelerate arbitrary Java UDFs without API changes. Unmodified applications run on GPUs/FPGAs, achieving up to 65×/184× speedups over CPU Flink. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13086
Venue
VLDB
Year
2022
Pagerank
5.4864891e-05
Overall Rank
8,104 | 44.40%
DOI
10.14778/3565838.3565842

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{xekalaki_vldb22,
        title = {{Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware}},
        author = {Xekalaki, Maria and Fumero, Juan and Stratikopoulos, Athanasios and Doka, Katerina and Katsakioris, Christos and Bitsakos, Constantinos and Koziris, Nectarios and Kotselidis, Christos},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {13},
        pages = {3869--3882},
        doi = {10.14778/3565838.3565842},
        url = {https://doi.org/10.14778/3565838.3565842},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,511 DPDPU: Data Processing with DPUs 2025 CIDR 5.25736e-05
9,873 Workload Placement on Heterogeneous CPU-GPU Systems 2024 VLDB 5.2043672e-05
10,691 Flux: Unifying Heterogeneous Infrastructure for Alibaba AnalyticDB 2025 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank Cited Paper Year Venue Pagerank
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