IncrCP: Decomposing and Orchestrating Incremental Checkpoints for Effective Recommendation Model Training
Summary: IncrCP does incremental checkpointing for massive recommender models by recording per-iteration changed parameters and their indexes into independent chunk files. A 2-D chunk orchestration plus selective extraction and concatenation reduces I/O/dedup and yields up to 6.6× faster recovery and ~60% storage reduction. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qingyin Lin
- 2. Jiangsu Du
- 3. Rui Li
- 4. Zhiguang Chen
- 5. Wenguang Chen
- 6. Nong Xiao
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,993 | DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud | 2024 | VLDB | 5.2415551e-05 |
| 5,998 | Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding | 2023 | VLDB | 5.2415551e-05 |
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