Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparison of Huawei Ascend, Biren, and Cambricon MLU
• Contrast 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 configuring the environment

Code Translation Techniques

• Migrating CUDA memory access patterns and kernel logic
• Mapping compute grid and thread models
• Automated versus manual translation methods

Platform-Specific Implementations

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

Cross-Platform Testing and Optimization

• Profiling execution on each target platform
• Comparing memory tuning and parallel execution
• Monitoring performance and iterative refinement

Managing Mixed GPU Environments

• Implementing hybrid deployments with multiple architectures
• Developing fallback strategies and device detection
• Using abstraction layers to enhance code maintainability

Case Studies and Best Practices

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

Summary and Next Steps