Migrating CUDA Applications to Chinese GPU Architectures Training Course

The Chinese AI GPU Ecosystem Landscape

• An overview of Huawei Ascend, Biren, and Cambricon MLU
• Contrast between CUDA and CANN, Biren SDK, and BANGPy models
• Market trends and vendor ecosystems

Getting Ready for Migration

• Evaluating your existing CUDA codebase
• Selecting target platforms and SDK versions
• Installing toolchains and configuring environments

Code Translation Methodologies

• Translating CUDA memory access patterns and kernel logic
• Aligning compute grid and thread models
• Exploring automated versus manual translation tools

Implementation on Specific Platforms

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

Testing and Optimizing Across Platforms

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

Managing Hybrid GPU Environments

• Implementing hybrid deployments with diverse architectures
• Establishing fallback strategies and device detection
• Creating abstraction layers for long-term code maintainability

Case Studies and Best Practices

• Migrating vision and NLP models to Ascend or Cambricon
• Adapting inference pipelines on Biren clusters
• Resolving version conflicts and API discrepancies

Wrap-up and Future Directions