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DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap with communication. DualPipe addresses that by scheduling micro-batches from both ends of the pipeline in a bidirectional fashion—i.e. some micro-batches flow forward while others flow backward—so that computation on one partition can coincide with communication for another.
Features
- Bidirectional scheduling of micro-batches to overlap forward and backward passes
- Reduction of pipeline bubbles and GPU idle times
- Support for composition with other parallelism strategies (MoE, EP, tensor parallelism)
- Python implementation of scheduling logic (dualpipe.py)
- Profile / diagnostics to measure computation-communication overlap and efficiency
- Open source and publicly released as part of DeepSeek’s open infrastructure initiative
Project Samples
