Migrating CUDA Applications to Chinese GPU Architectures Training Course

Introduction to the Chinese AI GPU Landscape

• Comparative analysis of Huawei Ascend, Biren, and Cambricon MLU
• Contrasting CUDA with CANN, Biren SDK, and BANGPy paradigms
• Current industry trends and vendor ecosystem dynamics

Preparation for Migration

• Evaluating the structure of your existing CUDA codebase
• Determining target platforms and corresponding SDK versions
• Setting up toolchains and development environments

Code Translation Methodologies

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

Platform-Specific Implementation Details

• Leveraging Huawei CANN operators and custom kernel development
• Utilizing the Biren SDK conversion pipeline
• Reconstructing models using BANGPy (Cambricon)

Cross-Platform Testing and Performance Tuning

• Profiling execution efficiency on each target platform
• Optimizing memory management and parallel execution strategies
• Monitoring performance and iterating on improvements

Handling Mixed GPU Environments

• Implementing hybrid deployments across multiple architectures
• Developing fallback strategies and device detection mechanisms
• Creating abstraction layers to enhance code maintainability

Case Studies and Industry Best Practices

• Migrating vision and NLP models to Ascend or Cambricon hardware
• Adapting inference pipelines for Biren clusters
• Resolving version mismatches and API discrepancies

Conclusion and Future Recommendations