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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.
A prediction-guided runtime that uses workflow forecasts to construct and optimize a physical execution graph and reduces end-to-end makespan and overall p95 completion latency under burst arrivals by up to 36.8% and 25.8% over state-of-the-art workflow schedulers.
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...
Deep neural networks (DNNs) with billions of parameters power many important applications, but their training is fundamentally constrained by the limited on-chip memory of GPUs. This memory wall forces training to rely on distributed execution or memory offloading, both of which introduce substantial inefficiencies. Ex...
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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