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A Distributed View from a Single GPU: RankLens for Distributed Training
Modern distributed training is costly to debug, tune, and analyze because faithful analysis often requires repeated target-scale executions. We observe that many analysis tasks depend on the original distributed execution path, but not on full all-rank execution. We present RankLens, a single-rank e
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