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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
- Assessing your current 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 and thread models
- Evaluating 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
- Conducting memory tuning and parallel execution comparisons
- Tracking performance and iterating on optimizations
Managing Mixed GPU Environments
- Handling hybrid deployments with multiple architectures
- Implementing fallback strategies and device detection
- Using abstraction layers to enhance code maintainability
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon
- Retrofitting inference pipelines on Biren clusters
- Managing version mismatches and API gaps
Summary and Next Steps
Requirements
- Practical experience in 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
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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