Migrating CUDA Applications to Chinese GPU Architectures Training Course

Overview of the Chinese AI GPU Ecosystem

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

Preparing for Migration

• Assessing your CUDA codebase.
• Identifying target platforms and SDK versions.
• Toolchain installation and environment setup.

Code Translation Techniques

• Porting CUDA memory access and kernel logic.
• Mapping compute grid/thread models.
• Evaluating automated vs. manual translation options.

Platform-Specific Implementations

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

Cross-Platform Testing and Optimization

• Profiling execution on each target platform.
• Comparing memory tuning and parallel execution.
• Tracking performance and iterating improvements.

Managing Mixed GPU Environments

• Hybrid deployments involving multiple architectures.
• Implementing fallback strategies and device detection.
• Utilizing abstraction layers to enhance code maintainability.

Case Studies and Best Practices

• Porting vision and NLP models to Ascend or Cambricon.
• Integrating inference pipelines into Biren clusters.
• Handling version mismatches and API gaps.

Summary and Next Steps