OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
arXiv:2509.23722v2 Announce Type: replace-cross Abstract: 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 thes
The paper addresses a critical challenge in LLM training efficiency at a time when computational demands for AI are rapidly escalating, making optimized resource utilization paramount.
Improved pipeline parallelism directly translates to faster and more cost-effective training of large AI models, impacting the pace of AI development and accessibility.
The co-optimization of partitioning, placement, and scheduling marks an advancement in overcoming efficiency bottlenecks for heterogeneous LLM architectures, leading to potentially significant performance gains.
- · AI model developers
- · Cloud computing providers
- · GPU manufacturers
- · Hyperscalers
- · Less efficient AI training approaches
- · Systems heavily reliant on older hardware architectures
Reduced training costs and time for large language models will accelerate their deployment and scale.
Faster innovation cycles in AI due to more efficient computational resource utilization.
Increased competition among AI developers as the barrier to training increasingly complex models is lowered.
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Read at arXiv cs.AI