Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparison of Huawei Ascend, Biren, and Cambricon MLUs.
• CUDA versus CANN, Biren SDK, and BANGPy programming models.
• Industry trends and vendor ecosystem dynamics.

Preparing for Migration

• Assessing the structure and dependencies of your CUDA codebase.
• Identifying target platforms and compatible SDK versions.
• Installing toolchains and configuring the development environment.

Code Translation Techniques

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

Platform-Specific Implementations

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

Cross-Platform Testing and Optimization

• Profiling execution performance on each target platform.
• Tuning memory usage and comparing parallel execution strategies.
• Implementing performance tracking and iterative improvement cycles.

Managing Mixed GPU Environments

• Deploying hybrid systems with multiple GPU architectures.
• Implementing fallback strategies and robust device detection.
• Using abstraction layers to enhance code maintainability.

Case Studies and Best Practices

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

Summary and Next Steps