Summary of Bitpipe: Bidirectional Interleaved Pipeline Parallelism For Accelerating Large Models Training, by Houming Wu et al.
BitPipe: Bidirectional Interleaved Pipeline Parallelism for Accelerating Large Models Training
by Houming Wu, Ling Chen, Wenjie Yu
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to accelerating large models training, dubbed BitPipe, is proposed. This method leverages bidirectional interleaved pipeline parallelism to reduce computational time and increase device utilization. A hybrid scheme combines fusing interleaved pipelines with bidirectional pipelines, while a V-shaped schedule enables eager gradient synchronization. Experiments demonstrate that BitPipe improves training throughput for GPT-style and BERT-style models by 1.05x-1.28x compared to state-of-the-art synchronous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BitPipe is a new way to train really big AI models. It makes use of special pipeline parallelism to make the training process faster and more efficient. This is achieved through a combination of bidirectional pipelines and a unique scheduling system. The result is that BitPipe can train models up to 1.28 times faster than other methods. |
Keywords
» Artificial intelligence » Bert » Gpt
