Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

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

Preparing for Migration

• Evaluating the existing CUDA codebase
• Defining target platforms and SDK versions
• Toolchain installation and environment configuration

Code Translation Techniques

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

Platform-Specific Implementations

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

Cross-Platform Testing and Optimization

• Profiling execution across target platforms
• Tuning memory and comparing parallel execution
• Monitoring performance and iterative improvement

Managing 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 Best Practices

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

Summary and Next Steps