Migrating CUDA Applications to Chinese GPU Architectures Training Course

Insights into the Chinese AI GPU Ecosystem

• Analyzing Huawei Ascend, Biren, and Cambricon MLU architectures.
• Contrasting CUDA with CANN, Biren SDK, and BANGPy frameworks.
• Market trends and the landscape of vendor ecosystems.

Readiness for Migration

• Conducting a review of your current CUDA codebase.
• Defining target platforms and required SDK versions.
• Setting up toolchains and configuring development environments.

Methodologies for Code Translation

• Adapting CUDA memory access patterns and kernel logic.
• Aligning compute grid and thread structures.
• Evaluating automated versus manual translation approaches.

Implementation on Specific Platforms

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

Testing and Optimization Across Platforms

• Profiling execution performance on each designated platform.
• Adjusting memory usage and comparing parallel execution strategies.
• Monitoring performance metrics and iterating on solutions.

Oversight of Mixed GPU Setups

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

Practical Examples and Industry Standards

• Porting vision and NLP models to Ascend or Cambricon.
• Adapting inference pipelines for Biren clusters.
• Mitigating version discrepancies and API limitations.

Conclusion and Future Directions