35 tagged kernel optimization, measured the same way as everything else here.
Browse within: cuda 35gpu-kernels 35high-performance-computing 35llm-agents 35
Skill Claude CodeCodex
Guide the agent through selecting the correct and efficient thread block dimensions and grid dimensions for a CUDA kernel, covering occupancy analysis, register and shared memory constraints, tail effects, persistent kernels, and when to use cudaOccupancyMaxActiveBlocksPerMultiprocessor as a decision tool.
Skill Claude CodeCodex
Guide the agent through implementing a Triton kernel that writes newly computed K and V tensors into a pre-allocated KV cache during LLM inference. This covers two cache layouts (contiguous and paged / vLLM-style PagedAttention), unified prefill and decode handling via a slotmapping tensor, GQA/MQA where the cache…
Skill Claude CodeCodex
Guide the agent through implementing a correct Triton kernel that applies Rotary Position Embeddings (RoPE) to query and key tensors before attention. This covers the two incompatible layout conventions (GPT-NeoX/HuggingFace-LLaMA vs GPT-J/original-paper), pre-computed cos/sin table consumption, per-token position…