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Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU.
- Analysis of CUDA versus CANN, Biren SDK, and BANGPy models.
- Industry trends and vendor ecosystems.
Preparing for Migration
- Assessing your CUDA codebase.
- Identifying target platforms and SDK versions.
- Installing the toolchain and setting up the environment.
Code Translation Techniques
- Porting CUDA memory access and kernel logic.
- Mapping compute grid/thread models.
- Evaluating automated versus manual translation options.
Platform-Specific Implementations
- Utilizing Huawei CANN operators and custom kernels.
- Understanding 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.
- Monitoring performance and iterating.
Managing Mixed GPU Environments
- Implementing hybrid deployments with multiple architectures.
- Establishing fallback strategies and device detection.
- Utilizing abstraction layers for code maintainability.
Case Studies and Best Practices
- Porting vision/NLP models to Ascend or Cambricon.
- Retrofitting inference pipelines on Biren clusters.
- Handling version mismatches and API gaps.
Summary and Next Steps
Requirements
- Experience programming with CUDA or GPU-based applications.
- Understanding of GPU memory models and compute kernels.
- Familiarity with AI model deployment or acceleration workflows.
Audience
- GPU programmers.
- System architects.
- Porting specialists.
21 Hours
