A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability

Problem
This paper addresses the limitations of existing pipeline-parallel training systems that rely on static or adaptively generated schedules, which can lead to inefficiencies due to runtime variability in computation and communication. Specifically, when task readiness diverges from the pre-committed execution order, stages may remain idle while other executable tasks are available. This issue is particularly relevant in the context of large-model training, where maximizing GPU utilization is critical. The work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose the Runtime-Readiness-First Pipeline (RRFP), a novel readiness-driven runtime for pipeline-parallel training. RRFP redefines the consumption of schedules, treating them as non-binding hints rather than strict sequences. This allows the system to prioritize currently ready tasks, thereby reducing idle time and improving overall throughput. Key components of RRFP include:

• Message-driven asynchronous communication: This facilitates efficient data transfer between pipeline stages without blocking.
• Lightweight tensor-parallel coordination: This ensures collective consistency across tensor operations while minimizing overhead.
• Ready-set arbitration: This mechanism allows for low-latency dispatch of tasks based on their readiness status.

The implementation of RRFP is integrated into a Megatron-based training framework, enabling it to leverage the architecture’s strengths while addressing the challenges posed by runtime variability.

Results
RRFP demonstrates significant performance improvements over fixed-order pipeline baselines across various settings. In experiments with language-only workloads, RRFP achieves up to a 1.77× speedup, while for multimodal workloads, the speedup reaches up to 2.77×. Furthermore, in cross-framework comparisons, RRFP utilizing the default BF hint outperforms a leading external system by up to 1.84×, all while maintaining training correctness. These results indicate that RRFP effectively mitigates the inefficiencies associated with traditional pipeline scheduling.

Limitations
The authors acknowledge that RRFP’s performance may be influenced by the specific characteristics of the workloads and the underlying hardware. They do not address potential scalability issues that may arise when extending the approach beyond 128 GPUs or in heterogeneous environments. Additionally, the reliance on message-driven communication may introduce overhead in scenarios with high inter-stage communication demands, which could offset some of the performance gains.

Why it matters
The introduction of RRFP has significant implications for the field of distributed training, particularly for large-scale models. By enabling more dynamic and efficient scheduling based on task readiness, RRFP can enhance GPU utilization and reduce training times, which is crucial for both research and production environments. This work opens avenues for further exploration into adaptive scheduling techniques and could influence the design of future pipeline-parallel training frameworks, potentially leading to more robust and efficient training paradigms.

Authors: Ruitao Liu, Xinyang Tian, Shuo Chen, Tingrui Zhang, Guang Yang, Alan Zhao, Wei Xu
Source: arXiv:2605.18750
URL: https://arxiv.org/abs/2605.18750v1