cuda-c-optimization

Reduce branch divergence in CUDA kernels by restructuring control flow to ensure threads within a warp follow similar execution paths. Apply structured debugging checklists and minimize global atomic operations to further improve computational throughput.

Maintain numerical stability in CUDA kernels by using reliable reduction algorithms, safe division, and square root functions. Apply specific techniques to prevent overflow and underflow when processing compute-heavy numerical tasks.

Use a structured debugging checklist for CUDA C kernels to validate grid configurations, check occupancy, and trace memory access issues. Follow a step-by-step guide to profile, adjust block sizes, and validate numerical stability in your code.

Low occupancy in CUDA kernels often stems from improper grid and block configurations or excessive register usage. Debug it by checking block sizing strategies and verifying memory access patterns to identify bank conflicts and divergence.