Migrating CUDA Applications to Chinese GPU Architectures Training Course

The Chinese AI GPU Ecosystem Landscape

• Contrasting Huawei Ascend, Biren, and Cambricon MLU architectures
• Differences between CUDA and CANN, Biren SDK, or BANGPy programming models
• Market trends and vendor ecosystem dynamics

Readiness for Migration

• Evaluating the complexity of your existing CUDA codebase
• Defining target platforms and selecting appropriate SDK versions
• Configuring toolchains and preparing development environments

Strategies for Code Translation

• Adapting CUDA memory management and kernel logic
• Mapping compute grid and thread structures
• Evaluating automated tools versus manual translation approaches

Implementation on Specific Platforms

• Leveraging Huawei CANN operators and developing custom kernels
• Utilising the Biren SDK conversion pipeline
• Reconstructing models using BANGPy (Cambricon)

Cross-Platform Validation and Tuning

• Profiling execution metrics on each target architecture
• Comparing memory optimisation and parallel execution strategies
• Monitoring performance and iterative improvement

Operationalising Mixed GPU Environments

• Designing hybrid deployments across multiple architectures
• Implementing fallback mechanisms and device discovery
• Utilising abstraction layers to enhance code maintainability

Case Studies and Industry Standards

• Porting vision and NLP models to Ascend or Cambricon platforms
• Adapting inference workflows for Biren clusters
• Resolving version incompatibilities and API discrepancies

Recap and Future Roadmap