NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
Summary: NuPS: a parameter server optimized for non-uniform parameter access. It selects per-parameter management techniques and adds sampling primitives with controlled quality–efficiency trade-offs to address skew and locality, yielding up to 10x speedups and linear scalability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexander Renz-Wieland (Technical University of Berlin)
- 2. Rainer Gemulla (University of Mannheim)
- 3. Zoi Kaoudi (Technical University of Berlin)
- 4. Volker Markl (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@inproceedings{renzwieland_sigmod22,
title = {{NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access}},
author = {Renz-Wieland, Alexander and Gemulla, Rainer and Kaoudi, Zoi and Markl, Volker},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517860},
url = {https://dl.acm.org/doi/10.1145/3514221.3517860},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,091 | Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads | 2024 | VLDB | 5.5871281e-05 |
| 7,356 | PetPS: Supporting Huge Embedding Models with Persistent Memory | 2023 | VLDB | 5.5289288e-05 |
| 9,691 | BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach | 2023 | SIGMOD | 5.1258854e-05 |
| 10,017 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 5.0825892e-05 |
| 10,897 | FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 22 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00056028465 |
| 464 | An Architecture for Parallel Topic Models | 2010 | VLDB | 0.0001776101 |
| 2,196 | Heterogeneity-aware Distributed Parameter Servers | 2017 | SIGMOD | 8.8807201e-05 |
| 2,737 | FlexPS: Flexible Parallelism Control in Parameter Server Architecture | 2018 | VLDB | 8.060868e-05 |
| 4,314 | Distributed Algorithms For Dynamic Replication Of Data | 1992 | PODS | 6.6547135e-05 |
| 4,911 | Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques | 2022 | VLDB | 6.3460622e-05 |
| 5,454 | PS2: Parameter Server on Spark | 2019 | SIGMOD | 6.1077156e-05 |
| 6,655 | Dynamic Parameter Allocation in Parameter Servers | 2020 | VLDB | 5.7036819e-05 |
| 8,511 | Just Move It! Dynamic Parameter Allocation in Action | 2021 | VLDB | 5.3116274e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 526 | PyTorch Distributed: Experiences on Accelerating Data Parallel Training | 2020 | VLDB |
| 2 | 5,454 | PS2: Parameter Server on Spark | 2019 | SIGMOD |
| 3 | 538 | MLbase: A Distributed Machine-learning System | 2013 | CIDR |
| 4 | 8,228 | SDP_PIPE: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training | 2023 | VLDB |
| 5 | 4,961 | Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce | 2021 | SIGMOD |
| 6 | 8,298 | Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale | 2022 | VLDB |
| 7 | 8,511 | Just Move It! Dynamic Parameter Allocation in Action | 2021 | VLDB |
| 8 | 2,196 | Heterogeneity-aware Distributed Parameter Servers | 2017 | SIGMOD |
| 9 | 2,737 | FlexPS: Flexible Parallelism Control in Parameter Server Architecture | 2018 | VLDB |
| 10 | 6,655 | Dynamic Parameter Allocation in Parameter Servers | 2020 | VLDB |