Migrating CUDA Applications to Chinese GPU Architectures Training Course

Introduction to the Chinese AI GPU Ecosystem

• 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.
• Selecting target platforms and verifying SDK versions.
• Installing the toolchain and configuring the development environment.

Code Translation Methodologies

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

Platform-Specific Integration

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

Inter-Platform Testing and Tuning

• Profiling execution efficiency on each target platform.
• Tuning memory usage and comparing parallel execution capabilities.
• Monitoring performance metrics and iterating on optimizations.

Handling Hybrid GPU Setups

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

Case Studies and Industry Best Practices

• Porting vision and NLP models to Ascend or Cambricon.
• Adapting inference pipelines for Biren clusters.
• Resolving version discrepancies and API gaps.

Conclusion and Future Directions