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Duration 21 hours
Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU
- Contrast between CUDA and CANN, Biren SDK, and BANGPy models
- Industry trends and vendor ecosystem dynamics
Preparing for Migration
- Assessing the structure of your existing CUDA codebase
- Identifying target platforms and specific SDK versions
- Installing toolchains and configuring the development environment
Code Translation Techniques
- Porting CUDA memory access patterns and kernel logic
- Mapping compute grid and thread models
- Exploring automated versus manual translation options
Platform-Specific Implementations
- Leveraging Huawei CANN operators and developing custom kernels
- Navigating the Biren SDK conversion pipeline
- Rebuilding models using BANGPy (Cambricon)
Cross-Platform Testing and Optimization
- Profiling execution performance on each target platform
- Tuning memory usage and comparing parallel execution capabilities
- Monitoring performance metrics and iterating on improvements
Managing Mixed GPU Environments
- Implementing hybrid deployments across multiple architectures
- Establishing fallback strategies and device detection mechanisms
- Utilizing abstraction layers to enhance code maintainability
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon platforms
- Retrofitting inference pipelines on Biren clusters
- Mitigating version mismatches and addressing API gaps
Summary and Next Steps
Requirements
- Practical experience programming with CUDA or GPU-based applications
- Comprehensive understanding of GPU memory models and compute kernels
- Familiarity with AI model deployment or acceleration workflows
Target Audience
- GPU programmers
- System architects
- Porting specialists
