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 frameworks
• Current industry trends and vendor ecosystem dynamics

Migration Preparation

• Evaluating the structure and requirements of your existing CUDA codebase
• Selecting target platforms and verifying SDK version compatibility
• Setting up the toolchain and development environment

Code Translation Methodologies

• Adapting CUDA memory access patterns and kernel logic
• Mapping compute grid and thread models to new architectures
• Exploring automated versus manual translation approaches

Implementing Platform-Specific Solutions

• Utilizing Huawei CANN operators and developing custom kernels
• Navigating the Biren SDK conversion pipeline
• Reconstructing models using BANGPy on Cambricon hardware

Testing and Optimizing Across Platforms

• Profiling execution performance on each target platform
• Tuning memory usage and comparing parallel execution efficiency
• Monitoring performance metrics and iterating on optimizations

Managing Mixed GPU Environments

• Deploying hybrid solutions with multiple GPU architectures
• Implementing fallback strategies and robust device detection
• Creating abstraction layers to enhance code maintainability

Case Studies and Best Practices

• Examples of porting vision and NLP models to Ascend or Cambricon
• Adapting inference pipelines for Biren clusters
• Mitigating version mismatches and addressing API discrepancies

Summary and Future Directions