Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

• Comparison of Huawei Ascend, Biren, and Cambricon MLU.
• CUDA vs. CANN, Biren SDK, and BANGPy models.
• Industry trends and vendor ecosystems.

Preparing for Migration

• Evaluating the existing CUDA codebase.
• Determining target platforms and SDK versions.
• Installing the toolchain and setting up the environment.

Code Translation Techniques

• Porting CUDA memory access and kernel logic.
• Mapping compute grid and thread models.
• Options for automated vs. manual translation.

Platform-Specific Implementations

• Leveraging Huawei CANN operators and custom kernels.
• The Biren SDK conversion pipeline.
• Rebuilding models using BANGPy (Cambricon).

Cross-Platform Testing and Optimization

• Profiling execution on each target platform.
• Memory tuning and comparing parallel execution.
• Performance tracking and iterative improvement.

Managing Mixed GPU Environments

• Hybrid deployments involving multiple architectures.
• Fallback strategies and device detection.
• Abstraction layers for enhanced code maintainability.

Case Studies and Best Practices

• Porting vision and NLP models to Ascend or Cambricon.
• Retrofitting inference pipelines on Biren clusters.
• Managing version mismatches and API gaps.

Summary and Next Steps