Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparative analysis of Huawei Ascend, Biren, and Cambricon MLUs
• Comparing CUDA against CANN, Biren SDK, and BANGPy programming models
• Current industry trends and vendor ecosystem dynamics

Preparing for Migration

• Evaluating the structure and complexity of your existing CUDA codebase
• Identifying target platforms and compatible SDK versions
• Setting up the toolchain and development environment

Code Translation Techniques

• Translating CUDA memory access patterns and kernel logic
• Mapping compute grid and thread models to target architectures
• Exploring automated versus manual translation approaches

Platform-Specific Implementations

• Leveraging Huawei CANN operators and custom kernel development
• Utilizing the Biren SDK conversion pipeline
• Rebuilding models using BANGPy on Cambricon hardware

Cross-Platform Testing and Optimization

• Profiling execution metrics on each target platform
• Comparing memory tuning and parallel execution strategies
• Tracking performance metrics and iterating for improvements

Managing Mixed GPU Environments

• Designing hybrid deployments across multiple architectures
• Implementing fallback strategies and robust device detection
• Creating abstraction layers to ensure long-term code maintainability

Case Studies and Best Practices

• Porting vision and NLP models to Ascend or Cambricon platforms
• Retrofitting inference pipelines on Biren clusters
• Addressing version mismatches and API discrepancies

Summary and Next Steps