# OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling

arXiv: https://arxiv.org/abs/2509.23722 · HTML: https://mlsanity.com/paper/2509.23722/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 61 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://mlsanity.com/paper/2309.14509/) (2023)
- [MiniMax-01: Scaling Foundation Models with Lightning Attention](https://mlsanity.com/paper/2501.08313/) (2025)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [DAPPLE: A Pipelined Data Parallel Approach for Training Large Models](https://mlsanity.com/paper/2007.01045/) (2020)
- [Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models](https://mlsanity.com/paper/2504.03624/) (2025)
- [Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines](https://mlsanity.com/paper/2107.06925/) (2021)
- [Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism](https://mlsanity.com/paper/2211.13878/) (2022)
- [Dynamic Tensor Rematerialization](https://mlsanity.com/paper/2006.09616/) (2020)
- [Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning](https://mlsanity.com/paper/2201.12023/) (2022)
- [Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency](https://mlsanity.com/paper/2308.15762/) (2023)
- [FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism](https://mlsanity.com/paper/2412.01523/) (2024)
- [WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training](https://mlsanity.com/paper/2503.17924/) (2025)
- [DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models](https://mlsanity.com/paper/2408.04275/) (2024)
- [ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation](https://mlsanity.com/paper/2405.14009/) (2024)
- [Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation](https://mlsanity.com/paper/2408.03505/) (2024)
- [AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness](https://mlsanity.com/paper/2210.07297/) (2022)
- [DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines](https://mlsanity.com/paper/2311.10418/) (2023)
- [Understanding Stragglers in Large Model Training Using What-if Analysis](https://mlsanity.com/paper/2505.05713/) (2025)

## Built on by (most cited first, 2 of 2)

- [NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining](https://mlsanity.com/paper/2604.06956/) (2026)
- [A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability](https://mlsanity.com/paper/2605.18750/) (2026)

## Abstract (arXiv)

Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing training efficiency. Prior approaches typically optimize a single phase of the pipeline schedule (i.e., partitioning, placement, or scheduling), leaving substantial pipeline bubbles. While promising, co-optimization poses three key challenges: (1) complex performance modeling, (2) a combinatorial search space, and (3) irregular execution orders. To address these challenges, we propose OctoPipe, a pipeline parallelism system to jointly optimize partitioning, placement, and scheduling. First, we build a graph-based pipeline simulator to model heterogeneous pipeline execution for co-optimization. Second, on top of the simulator, we develop an iterative bubble-aware tuner to efficiently explore the combinatorial search space. Third, we implement a unified pipeline executor that dynamically orchestrates computation and communication to support irregular execution orders without deadlocks while maximizing communication-computation overlap. Experiments show that OctoPipe achieves 1.09--1.49× throughput improvement over the state-of-the-art pipeline parallelism approaches across various heterogeneous model configurations and GPU cluster scales.