Analyzing Near-Network Hardware Acceleration with Co-Processing on DPUs
Summary: Empirical study of near-network hardware acceleration on PCIe DPUs, quantifying how performance depends on data types, task/query configurations, and DPU reconfiguration overhead. Micro-benchmarks of partial offloads and host–DPU co-processing reveal throughput vs reconfiguration trade-offs and offer practical integration guidance for data systems. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Dimitrios Giouroukis (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Dwi P. A. Nugroho (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Varun Pandey (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 4. Steffen Zeuch (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 5. Volker Markl (Berlin Institute for the Foundations of Learning and Data; German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
@article{giouroukis_vldb25,
title = {{Analyzing Near-Network Hardware Acceleration with Co-Processing on DPUs}},
author = {Giouroukis, Dimitrios and Nugroho, Dwi P. A. and Pandey, Varun and Zeuch, Steffen and Markl, Volker},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {13},
pages = {5689--5702},
doi = {10.14778/3773731.3773743},
url = {https://doi.org/10.14778/3773731.3773743},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 26 of 26 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,217 | Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware | 2022 | VLDB |
| 2 | 10,541 | Accelerating Stream Processing Engines via Hardware Offloading | 2026 | SIGMOD |
| 3 | 3,773 | Hardware-conscious Query Processing in GPU-accelerated Analytical Engines | 2019 | CIDR |
| 4 | 10,345 | Data Movement-Aware GPU Sharing for Data-Intensive Systems | 2026 | CIDR |
| 5 | 7,523 | Workload Placement on Heterogeneous CPU-GPU Systems | 2024 | VLDB |
| 6 | 5,347 | Query Co-Processing on Commodity Processors | 2006 | VLDB |
| 7 | 8,758 | DDS: DPU-optimized Disaggregated Storage | 2024 | VLDB |
| 8 | 6,516 | GPU-accelerated data management under the test of time | 2020 | CIDR |
| 9 | 9,687 | DPDPU: Data Processing with DPUs | 2025 | CIDR |
| 10 | 10,807 | dpKernels: Harvesting DPU Compute Resources for Data-path Efficiency in Cloud Data Processing | 2026 | VLDB |