Migrating CUDA Applications to Chinese GPU Architectures Training Course

The Chinese AI GPU Ecosystem Landscape

• A comparative analysis of Huawei Ascend, Biren, and Cambricon MLU
• Differences between CUDA and CANN, Biren SDK, and BANGPy paradigms
• Current industry trends and vendor ecosystem dynamics

Migrating with Confidence

• Conducting a thorough audit of your existing CUDA codebase
• Selecting appropriate target platforms and SDK versions
• Setting up the toolchain and development environment

Techniques for Code Translation

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

Implementing Platform-Specific Solutions

• Leveraging Huawei CANN operators and custom kernel development
• Utilizing the Biren SDK conversion pipeline
• Reconstructing models using BANGPy (Cambricon)

Testing and Optimizing Across Platforms

• Profiling execution behavior on each specific target platform
• Comparing memory tuning strategies and parallel execution efficiency
• Monitoring performance metrics and iterating on improvements

Overseeing Mixed GPU Environments

• Managing hybrid deployments that involve multiple architectures
• Implementing fallback mechanisms and dynamic device detection
• Building abstraction layers to ensure code maintainability

Real-World Applications and Best Practices

• Case studies on porting vision and NLP models to Ascend or Cambricon
• Adapting inference pipelines for Biren clusters
• Resolving version incompatibilities and API discrepancies

Wrap-Up and Future Directions