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Memory-Efficient Pipeline-Parallel DNN Training
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Many state-of-the-art ML results have been obtained by scaling up the number of parameters in existing models. However, parameters and activations for such large models often do not fit in the memory of a single accelerator device; this means that it is necessary to distribute training of large models over multiple accelerators. In this work, we propose PipeDream-2BW, a system that supports memory-efficient pipeline parallelism. PipeDream-2BW uses a novel pipelining and weight gradient coalescing strategy, combined with the double buffering of weights, to ensure high throughput, low memory footprint, and weight update semantics similar to data parallelism. In addition, PipeDream-2BW automatically partitions the model over the available hardware resources, while respecting hardware constraints such as memory capacities of accelerators and interconnect topologies. PipeDream-2BW can accelerate the training of large GPT and BERT language models by up to 20$\times$ with similar final model accuracy.
Forward citations
Cited by 4 Pith papers
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Efficient Training on Multiple Consumer GPUs with RoundPipe
RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on...
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GIFT: Geometry-Informed Low-precision Gradient Communication for LLM Pretraining
Transforming gradients into K-FAC-based coordinates before FP8 quantization reduces communication error and improves downstream task preservation over Euclidean FP8, with a 7.6% end-to-end speedup on 64 GH200 GPUs.
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A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
RRFP introduces a readiness-driven runtime for pipeline parallelism that uses schedules as hints and ready-set arbitration to improve utilization under runtime variability, reporting up to 2.77x speedup on multimodal ...
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Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
Piper introduces resource modeling and pipelined hybrid parallelism for MoE training, delivering 2-3.5X higher MFU than prior frameworks and 1.2-9X better all-to-all bandwidth.
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