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Ch7 其他（Others）
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19. AI Workload、NUMA、GPUDirect、Huge Pages
#CC-07-019
中
AI Workload
NUMA
GPUDirect
Huge Pages
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☆
What specific OS features are critical for high-performance AI/Machine Learning workloads?
A
Huge Pages / Large Pages support reduces TLB miss rates when training models with massive datasets.
B
NUMA (Non-Uniform Memory Access) awareness allows the OS to schedule threads on CPU cores that are physically closer to the memory bank holding the training data.
C
GPUDirect (or similar RDMA technologies) enables network adapters to move data directly to/from GPU memory, bypassing the CPU and system RAM.
D
AI Workloads typically prefer Fine-Grained Context Switching (frequent switching) to maximize CPU responsiveness during training.
E
Containerization (e.g., Docker/Kubernetes) is preferred over full Virtual Machines for AI deployment due to lower overhead and direct access to hardware drivers.
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