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DeepShare is a scheduler that uses a continuous tenant-assurance signal to coordinate these decisions at runtime to achieve a more advantageous utilization-QoS trade-off than optimizing quotas, scheduling, and resource sharing independently.
Concurrent multi-agent workflows expose future dependencies and serving-state requirements while running on heterogeneous GPU pools with time-varying load, model residency, and resource availability. The logical workflow defines the required computation, whereas its physical scheduling units, model-lifecycle actions, r...
This work presents SpecBox, a runtime built around speculative sandbox preallocation tailored for dynamic LLM agent execution pipelines, and implements keyword matching and streaming semantic embedding to enable intent-driven sandbox prewarming, which identifies pending tool execution demands mid-LLM token generation a...
A hierarchical search-space planning framework for GPU kernel optimization that delivers stronger overall implementation validity, sample efficiency, and optimization performance than existing training-free methods, while remaining competitive with the training-based CUDA-L1 without additional model training is propose...
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.
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