Migrating CUDA Applications to Chinese GPU Architectures Training Course

Introduction to the Chinese AI GPU Landscape

• Analytical comparison of Huawei Ascend, Biren, and Cambricon MLU
• Differences between CUDA and frameworks like CANN, Biren SDK, and BANGPy
• Current industry trends and vendor ecosystem dynamics

Pre-Migration Preparation

• Evaluating the structure of your existing CUDA codebase
• Defining target platforms and specific SDK versions
• Setting up toolchains and configuring development environments

Code Adaptation Strategies

• Translating CUDA memory access patterns and kernel logic
• Aligning compute grid and thread modeling approaches
• Exploring automated versus manual translation methodologies

Implementation for Specific Platforms

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

Validation and Performance Optimization Across Platforms

• Profiling execution efficiency on each target hardware platform
• Adjusting memory usage and comparing parallel execution outcomes
• Monitoring performance metrics and iterating on solutions

Administration of Heterogeneous GPU Setups

• Deploying hybrid configurations with mixed architectures
• Implementing fallback mechanisms and robust device detection
• Employing abstraction layers to ensure long-term code maintainability

Real-World Examples and Recommended Practices

• Migrating vision and NLP models to Ascend or Cambricon hardware
• Updating inference pipelines on Biren cluster environments
• Resolving version inconsistencies and API discrepancies

Conclusions and Future Directions