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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.07035 |
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| _version_ | 1866917070830043136 |
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| author | Zhang, Keyao Chen, Yiquan Hu, Zhuo Lin, Wenhai Xu, Jiexiong Chen, Wenzhi |
| author_facet | Zhang, Keyao Chen, Yiquan Hu, Zhuo Lin, Wenhai Xu, Jiexiong Chen, Wenzhi |
| contents | The accuracy of large language models (LLMs) improves with increasing model size, but increasing model complexity also poses significant challenges to training stability. Periodic checkpointing is a key mechanism for fault recovery and is widely used in LLM training. However, traditional checkpointing strategies often pause or delay GPU computation during checkpoint saving for checkpoint GPU-CPU transfer, resulting in significant training interruptions and reduced training throughput. To address this issue, we propose GoCkpt, a method to overlap checkpoint saving with multiple training steps and restore the final checkpoint on the CPU. We transfer the checkpoint across multiple steps, each step transfers part of the checkpoint state, and we transfer some of the gradient data used for parameter updates. After the transfer is complete, each partial checkpoint state is updated to a consistent version on the CPU, thus avoiding the checkpoint state inconsistency problem caused by transferring checkpoints across multiple steps. Furthermore, we introduce a transfer optimization strategy to maximize GPU-CPU bandwidth utilization and SSD persistence throughput. This dual optimization overlapping saves across steps and maximizing I/O efficiency significantly reduces invalid training time. Experimental results show that GoCkpt can increase training throughput by up to 38.4% compared to traditional asynchronous checkpoint solutions in the industry. We also find that GoCkpt can reduce training interruption time by 86.7% compared to the state-of-the-art checkpoint transfer methods, which results in a 4.8% throughput improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GoCkpt: Gradient-Assisted Multi-Step overlapped Checkpointing for Efficient LLM Training Zhang, Keyao Chen, Yiquan Hu, Zhuo Lin, Wenhai Xu, Jiexiong Chen, Wenzhi Operating Systems The accuracy of large language models (LLMs) improves with increasing model size, but increasing model complexity also poses significant challenges to training stability. Periodic checkpointing is a key mechanism for fault recovery and is widely used in LLM training. However, traditional checkpointing strategies often pause or delay GPU computation during checkpoint saving for checkpoint GPU-CPU transfer, resulting in significant training interruptions and reduced training throughput. To address this issue, we propose GoCkpt, a method to overlap checkpoint saving with multiple training steps and restore the final checkpoint on the CPU. We transfer the checkpoint across multiple steps, each step transfers part of the checkpoint state, and we transfer some of the gradient data used for parameter updates. After the transfer is complete, each partial checkpoint state is updated to a consistent version on the CPU, thus avoiding the checkpoint state inconsistency problem caused by transferring checkpoints across multiple steps. Furthermore, we introduce a transfer optimization strategy to maximize GPU-CPU bandwidth utilization and SSD persistence throughput. This dual optimization overlapping saves across steps and maximizing I/O efficiency significantly reduces invalid training time. Experimental results show that GoCkpt can increase training throughput by up to 38.4% compared to traditional asynchronous checkpoint solutions in the industry. We also find that GoCkpt can reduce training interruption time by 86.7% compared to the state-of-the-art checkpoint transfer methods, which results in a 4.8% throughput improvement. |
| title | GoCkpt: Gradient-Assisted Multi-Step overlapped Checkpointing for Efficient LLM Training |
| topic | Operating Systems |
| url | https://arxiv.org/abs/2511.07035 |