CUDA Best Practices

Guidelines and recommendations for writing efficient CUDA code.

Memory Management

  • Minimize host-device data transfers
  • Use pinned memory for faster transfers
  • Ensure proper memory alignment
  • Use shared memory for frequently accessed data
  • Implement proper memory coalescing

Kernel Optimization

  • Choose appropriate block sizes
  • Minimize thread divergence
  • Balance resource usage and occupancy
  • Use asynchronous operations when possible
  • Implement proper error checking

Code Organization

  • Separate device and host code
  • Use proper error handling macros
  • Implement clean-up routines
  • Document kernel launch parameters
  • Follow consistent naming conventions

Performance Considerations

  • Profile code to identify bottlenecks
  • Use appropriate data types
  • Consider using texture memory for 2D data
  • Implement proper synchronization
  • Optimize memory access patterns

Development Workflow

  • Start with working CPU code
  • Implement basic GPU version first
  • Profile and identify bottlenecks
  • Optimize incrementally
  • Validate results at each step