Unverified70% confidenceFactTime unknown
At the thousand-GPU scale, the latency of a single AllReduce operation can account for more than 30% of the per-step training time.
2
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
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Related Claims
UnverifiedSaving checkpoints too frequently slows training, while too sparse a schedule makes rollbacks costly; a common practice is to trigger on both a time interval (e.g., every 30 minutes) and a step interval, and to write to disk only from the main process to avoid multi-GPU concurrent write conflicts.76% similarUnverified仅调整 num_workers、pin_memory、prefetch_factor 三个 DataLoader 参数，相同 2000 步训练耗时从 633 秒降至 358 秒，运行时间缩短 43%74% similarUnverified在大模型训练中，通信开销往往占到总训练时间的30%以上71% similar
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