Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparison of Huawei Ascend, Biren, and Cambricon MLU
• Differences between CUDA and CANN, Biren SDK, and BANGPy models
• Industry trends and vendor ecosystems

Preparing for Migration

• Evaluating your existing CUDA codebase
• Selecting target platforms and SDK versions
• Installing toolchains and setting up the 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

• 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 on each target platform
• Comparing memory tuning and parallel execution
• Tracking performance and iterating on improvements

Managing Mixed GPU Environments

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

Case Studies and Best Practices

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

Summary and Next Steps