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Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU
- 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
- Setting up toolchains and environments
Code Translation Techniques
- Translating CUDA memory access and kernel logic
- Mapping compute grid and thread models
- Exploring 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
- Tracking performance and iterating
Managing Mixed GPU Environments
- Hybrid deployments involving multiple architectures
- Strategies for fallback and device detection
- Using abstraction layers to ensure code maintainability
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon
- Adapting inference pipelines on Biren clusters
- Addressing version mismatches and API gaps
Summary and Next Steps
Requirements
- Experience in programming with CUDA or GPU-based applications.
- Knowledge of GPU memory models and compute kernels.
- Familiarity with AI model deployment or acceleration workflows.
Audience
- GPU programmers
- System architects
- Porting specialists
21 Hours
