Mixtera: A Data Plane for Foundation Model Training
Summary: Mixtera: declarative data plane for foundation-model training, expressing sample mixtures and visitation order over arbitrary data properties atop existing collections. Centralized read-only layer supports dynamic, feedback-driven reweighting; scales to 256 GH200, no training bottleneck, and implements ADO. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Maximilian Böther (ETH Zurich)
- 2. Xiaozhe Yao (ETH Zurich)
- 3. Tolga Kerimoglu (ETH Zurich)
- 4. Dan Graur (ETH Zurich)
- 5. Viktor Gsteiger (ETH Zurich)
- 6. Ana Klimovic (ETH Zurich)
BibTeX Citation
@inproceedings{bother_sigmod26,
title = {{Mixtera: A Data Plane for Foundation Model Training}},
author = {Böther, Maximilian and Yao, Xiaozhe and Kerimoglu, Tolga and Graur, Dan and Gsteiger, Viktor and Klimovic, Ana},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786668},
url = {https://dl.acm.org/doi/10.1145/3786668},
year = {2026}
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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 |
|---|---|---|---|---|
| 103 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00034161428 |
| 361 | The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing | 2015 | VLDB | 0.00020138717 |
| 1,446 | Analyzing and Mitigating Data Stalls in DNN Training | 2021 | VLDB | 0.0001076818 |
| 2,018 | tf.data: A Machine Learning Data Processing Framework | 2021 | VLDB | 9.3001937e-05 |
| 2,473 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 8.5326287e-05 |
| 6,064 | Data-Juicer: A One-Stop Data Processing System for Large Language Models | 2024 | SIGMOD | 5.9895838e-05 |
| 8,905 | TensorSocket: Shared Data Loading for Deep Learning Training | 2026 | SIGMOD | 5.3483178e-05 |
| 8,906 | Unveiling Challenges for LLMs in Enterprise Data Engineering | 2026 | VLDB | 5.3483178e-05 |
| 8,907 | Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models | 2025 | SIGMOD | 5.3483178e-05 |
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