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Duration 21 hours
Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparing Huawei Ascend, Biren, and Cambricon MLU
- Contrasting CUDA with CANN, Biren SDK, and BANGPy models
- Exploring industry trends and vendor ecosystems
Migration Preparation
- Assessing your existing CUDA codebase
- Identifying target platforms and appropriate SDK versions
- Installing toolchains and setting up the environment
Code Translation Methodologies
- Porting CUDA memory access patterns and kernel logic
- Mapping compute grid and thread models
- Evaluating automated versus manual translation approaches
Platform-Specific Implementation Details
- Leveraging Huawei CANN operators and custom kernels
- Utilizing 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 strategies
- Monitoring performance and iterating for improvements
Managing Mixed GPU Environments
- Deploying hybrid solutions with multiple architectures
- Implementing fallback strategies and device detection
- Creating abstraction layers for enhanced code maintainability
Case Studies and Best Practices
- Migrating vision and NLP models to Ascend or Cambricon
- Adapting inference pipelines for Biren clusters
- Resolving version mismatches and API gaps
Summary and Next Steps
Requirements
- Hands-on experience programming with CUDA or GPU-based applications
- A solid understanding of GPU memory models and compute kernels
- Familiarity with AI model deployment or acceleration workflows
Audience
- GPU Programmers
- System Architects
- Porting Specialists
