arXiv:2602.02016v2 Announce Type: replace Abstract: Shampoo is one of the leading approximate second-order optimizers: a variant of it has won the MLCommons AlgoPerf competition, and it has been shown to produce models with lower activation outliers that are easier to compress. Yet, applying Shampoo currently comes at the cost of significant computational slowdown, due to its expensive internal operations. In this paper, we take a significant step to address this shortcoming by proposing \method (for \textbf{D}istributed \textbf{A}ccelerated \textbf{SH}ampoo), a faster implementation of Distri
The continuous drive for more efficient AI training and optimization algorithms is crucial as model sizes and computational costs rapidly increase, making improvements like DASH particularly timely.
Improved optimizers like DASH make large-scale AI training faster and more efficient, reducing resource consumption and accelerating the development of advanced AI models.
The computational bottleneck in applying advanced second-order optimizers like Shampoo is significantly reduced, opening the door for their wider adoption in production environments.
- · AI researchers
- · Hyperscalers
- · AI model developers
- · Compute infrastructure providers
- · Developers of less efficient optimization algorithms
- · Organizations with limited compute budgets
Faster and cheaper training of state-of-the-art AI models, including potentially very large models.
Accelerated AI progress across various applications due to reduced iteration times and lower operational costs for training.
Increased accessibility to advanced AI research and deployment for entities with fewer resources, potentially democratizing AI development slightly.
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Read at arXiv cs.LG