What is recorded here?
Split training batches into micro-batches and pipelined layer partitions over accelerators to train larger sequential networks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
First preprint appeared in 2018; NeurIPS publication was 2019. Pipeline bubbles and partition balance remain; source performance is not universally linear scaling.
Read the original work
- GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
- Huang and colleagues: GPipe micro-batch pipeline parallelism — institutional research record
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People and systems
Yanping Huang; Youlong Cheng; Ankur Bapna; Orhan Firat; Mia Xu Chen; Dehao Chen; HyoukJoong Lee; Jiquan Ngiam; Quoc V. Le; Yonghui Wu; Zhifeng Chen
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Huang and colleagues: GPipe micro-batch pipeline parallelism
Split training batches into micro-batches and pipelined layer partitions over accelerators to train larger sequential networks.
Scientific scope
First preprint appeared in 2018; NeurIPS publication was 2019. Pipeline bubbles and partition balance remain; source performance is not universally linear scaling.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Yanping Huang; Youlong Cheng; Ankur Bapna; Orhan Firat; Mia Xu Chen; Dehao Chen; HyoukJoong Lee; Jiquan Ngiam; Quoc V. Le; Yonghui Wu; Zhifeng Chen
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.