arXiv Artificial Intelligence

Accelerating Sharded Data Parallelism at Scale with Federated Learning

3 hours ago 18.09.2026 07:00 👁 2 views
Accelerating Sharded Data Parallelism at Scale with Federated Learning

Quick summary

arXiv:2609.20359v1 Announce Type: cross Abstract: The symbiotic scaling of artificial intelligence models and high-performance computing systems continually creates algorithmic challenges in their convergence. Foundation models (FMs) are a crucial example, requiring months-long training on thousands of cutting-edge GPUs. Sharded data parallelism (DP) is the dominant strategy to accelerate such computations by splitting data and models across multiple GPUs. However, it incurs prohibitive communication overhead when deployed at scale, particularly on multi-tier interconnects with heterogeneous p

Key takeaways

  • arXiv:2609.20359v1 Announce Type: cross Abstract: The symbiotic scaling of artificial intelligence models and high-performance computing systems continually creates algorithmic challenges in their convergence.
  • Foundation models (FMs) are a crucial example, requiring months-long training on thousands of cutting-edge GPUs.
  • Sharded data parallelism (DP) is the dominant strategy to accelerate such computations by splitting data and models across multiple GPUs.

Why it matters

“Accelerating Sharded Data Parallelism at Scale with Federated Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗