arXiv:2606.03498v1 Announce Type: new Abstract: Training modern machine learning models increasingly requires computation to be distributed across many accelerators. Data parallelism remains the default choice and is often paired with tensor-parallel sharding, but model parallelism becomes unavoidable once parameters, activations, or optimizer states no longer fit on a single device. This paper studies pipeline model parallelism through the lens of PipeDream (PD) (Harlap et al., 2018). Our first contribution is theoretical: we introduce Randomized PipeDream (RPD), a stale block-SGD abstraction
The increasing scale of machine learning models is making model parallelism unavoidable, and theoretical advancements like this are crucial for optimizing distributed training. This paper provides foundational theory for a significant existing method, PipeDream, at a time when computational efficiency is paramount for AI scaling.
This research provides theoretical grounding for pipeline parallelism, a key technique for training very large AI models, potentially leading to more efficient and scalable model development. Improved understanding and optimization of distributed training methods can accelerate the progress of AI capabilities and expand the scale of deployable models.
The theoretical framework for PipeDream offers a deeper understanding of its mechanisms and opens avenues for more robust and efficient distributed training of large-scale AI models. This could lead to practical improvements in machine learning infrastructure and the ability to train even larger, more complex systems economically.
- · Large AI model developers
- · Cloud infrastructure providers
- · Hardware manufacturers (GPUs, interconnects)
- · AI research institutions
- · AI developers reliant on single-device training
- · Inefficient distributed computing solutions
- · Smaller AI firms without access to advanced scaling techniques
More efficient training of massive AI models becomes feasible, reducing time and cost for development.
The ability to train larger models might lead to new capabilities and applications that were previously computationally intractable.
Increased accessibility to train and deploy advanced AI models could further centralize AI development among those with significant compute resources, potentially intensifying the compute supply chain demands.
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Read at arXiv cs.LG