AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Concord, a novel GPU sharing-enabled workload scheduler that outperforms state-of-the-art schedulers, achieves a 1.68 × reduction in JCT and a 29% improvement in GPU utilization in high-load scenarios.
ElastiCo is presented, an elastic co-location framework that enables training and inference workloads to safely share GPUs through three integrated mechanisms that decomposes the resulting multi-resource allocation problem into per-job configuration selection subproblems via dynamic per-resource shadow prices.
Current GPU Collective Communication Libraries (CCLs) employ predefined schedules optimized for stable environments. Their supported schedules and selection logic are fixed at communicator initialization, which fails to account for evolving runtime conditions, such as workload characteristics and hardware health status...
SA-Scheduler is presented, a structure-derived bottleneck-aware scheduling framework for multitasking MCM-GPUs that determines chip placement without hardware modification or runtime bottleneck profiling, and provides a principled and scalable foundation for multitasking on future MCM-GPUs.
This work presents a data-driven framework that leverages historical job traces to estimate the impact of resource modifications on queue performance, and introduces the Weighted Wait-Time Score (WWS), a bounded metric that captures both typical and tail wait-time behavior.
It is proved that optimal admission ordering is NP-hard under multi-dimensional resource demands via reduction from vector bin packing, and that optimal admission ordering is NP-hard under multi-dimensional resource demands via reduction from vector bin packing.
LEVELLER is proposed, the first communication scheduling system that achieves max-min fairness specifically for DLT workloads and introduces a novel online metric, normalized progress rate, which quantifies training experience by measuring actual progress against a contention-free ideal.
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