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