Methods › General › Asynchronous Pipeline Parallel › Pipelined Backpropagation
Pipelined Backpropagation
Introduced by Atli Kosson et al. in Pipelined Backpropagation at Scale: Training Large Models without Batches
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Pipelined Backpropagation is an asynchronous pipeline parallel training algorithm. It was first introduced by Petrowski et al (1993). It avoids fill and drain overhead by updating the weights without draining the pipeline first. This results in weight inconsistency, the use of different weights on the forward and backward passes for a given micro-batch. The weights used to produce a particular gradient may also have been updated when the gradient is applied, resulting in stale (or delayed) gradients. For these reasons PB resembles Asynchronous SGD and is not equivalent to standard SGD. Finegrained pipelining increases the number of pipeline stages and hence increases the weight inconsistency and delay.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Pipelined Backpropagation at Scale: Training Large Models without Batches 25 Mar 2020 · 0 repositories · arXiv:2003.11666
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 1 |
| Stochastic Optimization | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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