Summary of Pipeline Parallelism with Controllable Memory, by Penghui Qi et al.
Pipeline Parallelism with Controllable Memory
by Penghui Qi, Xinyi Wan, Nyamdavaa Amar, Min Lin
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 This paper proposes a framework to decompose pipeline schedules as repeating building blocks, revealing that the lifespan of these blocks decides the peak activation memory. Analysis shows that most existing schedules are memory inefficient, prompting the introduction of memory-efficient building blocks with controllable activation memory. These novel blocks reduce peak activation memory by up to 2/3 without sacrificing efficiency or throughput. The paper demonstrates a significant performance improvement in pure pipeline parallelism settings and hybrid scenarios for large language models. The proposed methods outperform the 1F1B baseline by up to 55% in terms of throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computer pipelines more efficient. Computer pipelines are like factories that process lots of information at once. The problem is that most current pipeline designs don’t use memory very well, which slows them down. The authors came up with a new way to design pipelines that uses memory better. This makes the pipelines faster and more efficient. They tested their method on big language models and found it performed 16% better than the usual way of doing things. |
Keywords
» Artificial intelligence » Prompting
