AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Characterizing LLM Kernel Access and Memory Interaction in Multi-Partition NUMA GPUs
This work categorizes LLM kernel operands into three inter-workgroup sharing patterns and shows that the required optimization strategies differ across categories, from simple per-workgroup pinning to subgroup-aware co-scheduling, highlighting the need for placement-aware kernel programming and smarter architectural support for work and data locality in multi-partition GPUs.