DECK: Experiences on Delta Checkpointing for Industrial Recommendation Systems
Summary: DECK introduces a production-ready delta checkpointing system for multi‑terabyte industrial recommender training that extracts and streams model-state deltas with near-zero overhead and without halting training. Decoupled optimal merging of streamed deltas yields ~12× checkpoint frequency with negligible throughput loss. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Xin Gao
- 2. Sibasish Acharya
- 3. Sihui Han
- 4. Yongxiong Ren
- 5. Yanli Zhao
- 6. Liang Luo
- 7. Chucheng Wang
- 8. Pradeep Fernando
- 9. Saurabh Mishra
- 10. Siqi Yan
- 11. Yicong Du
- 12. Elzbieta Krepska
- 13. Intaik Park
- 14. Min Ni
- 15. Qunshu Zhang
- 16. Shen Li
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 537 | PyTorch Distributed: Experiences on Accelerating Data Parallel Training | 2020 | VLDB | 0.00016963761 |
| 2,699 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 8.2937323e-05 |
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