Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparing Huawei Ascend, Biren, and Cambricon MLU architectures
• Contrasting CUDA with CANN, Biren SDK, and BANGPy programming models
• Examining industry trends and vendor ecosystems

Preparing for Migration

• Evaluating your existing CUDA codebase
• Identifying target platforms and specific SDK versions
• Installing toolchains and setting up the development environment

Code Translation Techniques

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

Platform-Specific Implementations

• Leveraging Huawei CANN operators and custom kernels
• Utilizing 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
• Tracking performance metrics and iterating on improvements

Managing Mixed GPU Environments

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

Case Studies and Best Practices

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

Summary and Next Steps