Migrating CUDA Applications to Chinese GPU Architectures Training Course

The Landscape of Chinese AI GPUs

• Comparative analysis of Huawei Ascend, Biren, and Cambricon MLU
• Differences between CUDA and CANN, Biren SDK, and BANGPy paradigms
• Market trends and vendor ecosystems

Migration Preparation

• Conducting an assessment of your current CUDA codebase
• Determining target platforms and corresponding SDK versions
• Configuring toolchains and development environments

Techniques for Code Translation

• Adapting CUDA memory access patterns and kernel logic
• Translating compute grid and thread models
• Evaluating automated versus manual translation methods

Implementations Specific to Platforms

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

Testing and Optimization Across Platforms

• Profiling execution efficiency on each intended platform
• Optimizing memory usage and comparing parallel execution models
• Monitoring performance metrics and iterative refinement

Handling Heterogeneous GPU Environments

• Deploying hybrid systems with multiple architectural types
• Implementing fallback mechanisms and device identification
• Establishing abstraction layers to ensure code maintainability

Real-World Examples and Best Practices

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

Conclusion and Forward-Looking Steps