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Found 2 papers, 1 papers with code
RINAS: Training with Dataset Shuffling Can Be General and Fast
However, loading shuffled data for large datasets incurs significant overhead in the deep learning pipeline and severely impacts the end-to-end training throughput.
RTP: Rethinking Tensor Parallelism with Memory Deduplication
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models.
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