arXiv:2606.09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communication using two portable runtime controls: shared-memory-driven occupancy shaping for computation ker
The increasing scale of machine learning models and distributed training architectures necessitates more efficient resource utilization to overcome communication bottlenecks.
Optimizing multi-GPU ML workloads directly accelerates AI development, reducing training times and computational costs, thus impacting the pace of AI innovation and deployment.
New portable runtime controls will enable better overlap of computation and communication, improving the efficiency and throughput of large-scale AI training systems.
- · AI compute providers
- · Large language model developers
- · Cloud infrastructure providers
- · GPU manufacturers
- · Inefficient distributed ML frameworks
- · Companies with outdated compute infrastructure
Faster and cheaper training of large AI models becomes possible.
Increased accessibility to train larger, more complex AI models, potentially leading to new breakthroughs.
Reduced barriers to entry for advanced AI development, fueling greater competition and innovation in the AI sector.
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Read at arXiv cs.AI