Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Domestic AI GPU Ecosystem

• Comparison of Huawei Ascend, Biren, and Cambricon MLU
• Contrasting CUDA with CANN, Biren SDK, and BANGPy frameworks
• Industry trends and vendor ecosystems

Preparation for Migration

• Evaluating your CUDA codebase
• Identifying target platforms and SDK versions
• Installing toolchains and setting up environments

Code Translation Techniques

• Porting CUDA memory access patterns and kernel logic
• Mapping compute grid and thread models
• Exploring automated versus manual translation options

Platform-Specific Implementations

• Utilizing Huawei CANN operators and custom kernels
• Understanding the Biren SDK conversion pipeline
• Rebuilding models using BANGPy (Cambricon)

Cross-Platform Testing and Optimization

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

Managing Mixed GPU Environments

• Implementing hybrid deployments across multiple architectures
• Establishing fallback strategies and device detection mechanisms
• Utilizing abstraction layers for improved code maintainability

Case Studies and Best Practices

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

Summary and Next Steps