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 versus CANN, Biren SDK, and BANGPy models.
• Industry trends and vendor ecosystems.

Preparing for Migration

• Evaluating your CUDA codebase.
• Identifying target platforms and SDK versions.
• Installing toolchains and setting up the environment.

Code Translation Techniques

• Translating CUDA memory access and kernel logic.
• Mapping compute grid and thread models.
• Evaluating automated versus manual translation options.

Platform-Specific Implementations

• Utilizing Huawei CANN operators and custom kernels.
• Implementing 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 strategies.
• Tracking performance and iterating improvements.

Managing Mixed GPU Environments

• Executing hybrid deployments with multiple architectures.
• Developing fallback strategies and device detection mechanisms.
• Implementing abstraction layers to enhance code maintainability.

Case Studies and Best Practices

• Translating vision and NLP models to Ascend or Cambricon.
• Integrating inference pipelines within Biren clusters.
• Addressing version mismatches and API gaps.

Summary and Next Steps