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        "description": "Atomic GPU-to-host snapshots still impose coordination/stall costs; overlapping checkpoints are constrained. Quantization is lossy, so preserved tested quality does not mean bitwise-identical resumed training. Historical checkpoint-size prediction and sampled quantization tuning are heuristics, not guarantees for all failure patterns or distributions.",
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      "statement": "# Check-N-Run: differential and quantized recommendation checkpoints\n\nTracked changed embedding rows, chose intermittent full versus differential checkpoints, and quantized snapshots in background CPU processes while coordinating trainer and data-reader recovery state.\n\n## Scientific scope\n\nAtomic GPU-to-host snapshots still impose coordination/stall costs; overlapping checkpoints are constrained. Quantization is lossy, so preserved tested quality does not mean bitwise-identical resumed training. Historical checkpoint-size prediction and sampled quantization tuning are heuristics, not guarantees for all failure patterns or distributions.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAssaf Eisenman; Kiran Kumar Matam; Steven Ingram; Dheevatsa Mudigere; Raghuraman Krishnamoorthi; Krishnakumar Nair; Misha Smelyanskiy; Murali Annavaram\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models](https://www.usenix.org/system/files/nsdi22-paper-eisenman.pdf)\n",
      "summary": "Tracked changed embedding rows, chose intermittent full versus differential checkpoints, and quantized snapshots in background CPU processes while coordinating trainer and data-reader recovery state.",
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