Migrating CUDA Applications to Chinese GPU Architectures Training Course

Introduction to the Chinese AI GPU Landscape

• Detailed comparison of Huawei Ascend, Biren, and Cambricon MLU architectures.
• Contrasting CUDA with CANN, Biren SDK, and BANGPy paradigms.
• Analysis of current industry trends and vendor ecosystems.

Migration Preparation

• Auditing the current CUDA codebase for migration readiness.
• Selecting appropriate target platforms and SDK versions.
• Setting up the toolchain and development environment.

Code Translation Strategies

• Translating CUDA memory access patterns and kernel logic.
• Adapting compute grid and thread model mappings.
• Evaluating automated versus manual translation approaches.

Platform-Specific Execution

• Leveraging Huawei CANN operators and custom kernel development.
• Implementing the Biren SDK conversion pipeline.
• Reconstructing models using BANGPy on Cambricon hardware.

Cross-Platform Validation and Tuning

• Profiling execution efficiency on each target architecture.
• Comparing memory tuning and parallel execution strategies.
• Continuous performance tracking and iterative refinement.

Orchestrating Mixed GPU Environments

• Designing hybrid deployments across multiple architectures.
• Implementing fallback mechanisms and device detection logic.
• Establishing abstraction layers to ensure long-term code maintainability.

Real-World Scenarios and Standards

• Case study: Porting Vision/NLP models to Ascend or Cambricon.
• Case study: Integrating inference pipelines on Biren clusters.
• Managing version discrepancies and API limitations.

Conclusion and Future Directions