Migrating CUDA Applications to Chinese GPU Architectures Training Course

Insights into the Chinese AI GPU Landscape

• Contrasting Huawei Ascend, Biren, and Cambricon MLUs
• Differences between CUDA and CANN, Biren SDK, and BANGPy paradigms
• Market trends and vendor ecosystems

Migration Readiness

• Reviewing your existing CUDA codebase
• Selecting target platforms and corresponding SDK versions
• Setting up the toolchain and development environment

Code Conversion Strategies

• Translating CUDA memory access patterns and kernel logic
• Aligning compute grid and thread models
• 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 on individual target systems
• Tuning memory usage and comparing parallel execution strategies
• Monitoring performance and iterating for improvements

Overseeing Hybrid GPU Setups

• Deploying solutions across multiple architectures
• Defining fallback mechanisms and device detection logic
• Implementing abstraction layers to enhance code maintainability

Real-World Scenarios and Industry Standards

• Migrating vision and NLP models to Ascend or Cambricon
• Adapting inference pipelines for Biren clusters
• Resolving version conflicts and API inconsistencies

Recap and Future Directions