Scaling Ray for AI workloads to 10k node clusters

Ray Core improvements target scaling bottlenecks in data pipelines and large AI training workloads, where millions of objects, tens of thousands of actors, and clusters up to 10,000 nodes exposed lock contention, overloaded control threads, and scheduling based on stale resource information. For Ray Data batch inference and shuffle, changes such as moving publishing work to a background thread, reducing unnecessary callbacks and object-location subscriptions, and optimizing local-object handling increased sustained task throughput by up to 1.6×, producing 23% faster batch inference and 24% faster shuffle performance on 500 nodes. For large reinforcement-learning and training clusters, Ray made its Global Control Service multithreaded, reduced control-plane work, removed an unnecessary scheduling reset for placement groups, and replaced dummy readiness tasks with direct asynchronous queries, enabling placement groups to become ready 62× faster at 2,000 nodes and 303× faster at 10,000 nodes. The updated system can support 40,000 actors on a 10,000-node cluster and launches actors 6.5× faster at 2,000 nodes, while ongoing work seeks to centralize more scheduling decisions and move non-detached actor lifecycle management closer to their owners to further reduce startup and recovery delays.

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