속성:Evidence citation
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technical review smartlmk a memory reclamation scheme for improving user perceived app launch ti e990e433 +
Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
technical review transparent and selective real time interrupt services for performance improvem b78f264b +
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technical review transparently bridging semantic gap in cpu management for virtualized environme ddf55f9b +
Primary or official publication source used for the technical review. +
technical review transparently exploiting device reserved memory for application performance in d2f8d46b +
Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
technical review virtual asymmetric multiprocessor for interactive performance of consolidated d 70c3d6f5 +
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Primary or official publication source used for the technical review. +
Primary or official publication source used for the technical review. +
Mark Zhao et al., "Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training", USENIX ATC 2023. +
Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training. USENIX ATC 2023. +
Mark Zhao et al., "Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training", USENIX ATC 2023. +
Alan Nair et al., "Telescope: Telemetry for Gargantuan Memory Footprint Applications", USENIX ATC 2024. +
Alan Nair et al., "Telescope: Telemetry for Gargantuan Memory Footprint Applications", USENIX ATC 2024. +
Telescope: Telemetry for Gargantuan Memory Footprint Applications. USENIX ATC 2024. +
the cost of dynamic reasoning demystifying ai agents and test time scaling from an ai infrastruc 6d6dff58 +
The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective. arXiv 2025. +