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CUDA Advanced Optimization — Deep Dive

Five techniques used by GPU engineers at NVIDIA, OpenAI, and Meta to push inference and HPC kernels to hardware limits. These go far beyond basic CUDA programming — they are what separates a "working" GPU kernel from a "production" one.

Why These Techniques Matter

Naive CUDA kernel:         ~30% of hardware peak  (common)
With kernel fusion:        ~50% of hardware peak
With CUDA Graphs:          +10-30% latency reduction
With cooperative groups:   enables algorithms impossible without them
With persistent kernels:   near-zero kernel launch overhead
With warp specialization:  ~70-85% of hardware peak  (elite)

Every LLM inference engine (TensorRT-LLM, vLLM, FasterTransformer, FlashAttention) uses all five.

Topic Index

# Topic What It Solves
01 CUDA Graphs CPU launch overhead kills latency at small batch sizes
02 Cooperative Groups Thread block boundary limits synchronization flexibility
03 Persistent Kernels Repeated kernel launches waste SM setup time
04 Kernel Fusion Separate kernels waste HBM bandwidth on intermediate results
05 Warp Specialization Compute and memory latency are not overlapped inside a kernel

How They Relate

CUDA Graphs          → reduces CPU↔GPU interface overhead
Cooperative Groups   → enables flexible intra-kernel synchronization
Persistent Kernels   → eliminates kernel launch overhead entirely
Kernel Fusion        → reduces HBM round-trips between operations
Warp Specialization  → overlaps compute and memory within a single kernel

Combined (e.g. FlashAttention-3):
  Persistent kernel + warp specialization + cooperative groups
  → 90%+ of H200 BF16 peak on attention kernels

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